inferpy.models package¶

Submodules¶

inferpy.models.parameter module¶

class inferpy.models.parameter.Parameter(initial_value, name=None)[source]¶

Bases: object

Random Variable parameter which can be optimized by an inference mechanism.

inferpy.models.prob_model module¶

class inferpy.models.prob_model.ProbModel(builder)[source]¶

Bases: object

Class that implements the probabilistic model functionality. It is composed of a graph, capturing the variable relationships, an OrderedDict containing the Random Variables/Parameters in order of creation, and the function which declare the Random Variables/Parameters.

expand_model(size=1)[source]¶: Create the expanded model vars using size as plate size and return the OrderedDict

fit(data, inference_method)[source]¶

plot_graph()[source]¶

posterior(target_names=None, data={})[source]¶

posterior_predictive(target_names=None, data={})[source]¶

prior(target_names=None, data={}, size_datamodel=1)[source]¶

inferpy.models.prob_model.probmodel(builder)[source]¶: Decorator to create probabilistic models. The function decorated must be a function which declares the Random Variables in the model. It is not required that the function returns such variables (they are captured using ed.tape).

inferpy.models.random_variable module¶

inferpy.models.random_variable.Autoregressive(*args, **kwargs)¶

Create a random variable for Autoregressive.

See Autoregressive for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct an Autoregressive distribution.

Parameters:

distribution_fn – Python callable which constructs a tfd.Distribution-like instance from a Tensor (e.g., sample0). The function must respect the “autoregressive property”, i.e., there exists a permutation of event such that each coordinate is a diffeomorphic function of on preceding coordinates.
sample0 – Initial input to distribution_fn; used to build the distribution in __init__ which in turn specifies this distribution’s properties, e.g., event_shape, batch_shape, dtype. If unspecified, then distribution_fn should be default constructable.
num_steps – Number of times distribution_fn is composed from samples, e.g., num_steps=2 implies distribution_fn(distribution_fn(sample0).sample(n)).sample().
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class. Default value: “Autoregressive”.

Raises:

ValueError – if num_steps and num_elements(distribution_fn(sample0).event_shape) are both None.
ValueError – if num_steps < 1.

inferpy.models.random_variable.BatchReshape(*args, **kwargs)¶

Create a random variable for BatchReshape.

See BatchReshape for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct BatchReshape distribution.

Parameters:

distribution – The base distribution instance to reshape. Typically an instance of Distribution.
batch_shape – Positive int-like vector-shaped Tensor representing the new shape of the batch dimensions. Up to one dimension may contain -1, meaning the remainder of the batch size.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – The name to give Ops created by the initializer. Default value: “BatchReshape” + distribution.name.

Raises:

ValueError – if batch_shape is not a vector.
ValueError – if batch_shape has non-positive elements.
ValueError – if batch_shape size is not the same as a distribution.batch_shape size.

inferpy.models.random_variable.Bernoulli(*args, **kwargs)¶

Create a random variable for Bernoulli.

See Bernoulli for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Bernoulli distributions.

Parameters:

logits – An N-D Tensor representing the log-odds of a 1 event. Each entry in the Tensor parametrizes an independent Bernoulli distribution where the probability of an event is sigmoid(logits). Only one of logits or probs should be passed in.
probs – An N-D Tensor representing the probability of a 1 event. Each entry in the Tensor parameterizes an independent Bernoulli distribution. Only one of logits or probs should be passed in.
dtype – The type of the event samples. Default: int32.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – If p and logits are passed, or if neither are passed.

inferpy.models.random_variable.Beta(*args, **kwargs)¶

Create a random variable for Beta.

See Beta for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Beta distributions.

Parameters:

concentration1 – Positive floating-point Tensor indicating mean number of successes; aka “alpha”. Implies self.dtype and self.batch_shape, i.e., concentration1.shape = [N1, N2, …, Nm] = self.batch_shape.
concentration0 – Positive floating-point Tensor indicating mean number of failures; aka “beta”. Otherwise has same semantics as concentration1.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Binomial(*args, **kwargs)¶

Create a random variable for Binomial.

See Binomial for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Binomial distributions.

Parameters:

total_count – Non-negative floating point tensor with shape broadcastable to [N1,…, Nm] with m >= 0 and the same dtype as probs or logits. Defines this as a batch of N1 x … x Nm different Binomial distributions. Its components should be equal to integer values.
logits – Floating point tensor representing the log-odds of a positive event with shape broadcastable to [N1,…, Nm] m >= 0, and the same dtype as total_count. Each entry represents logits for the probability of success for independent Binomial distributions. Only one of logits or probs should be passed in.
probs – Positive floating point tensor with shape broadcastable to [N1,…, Nm] m >= 0, probs in [0, 1]. Each entry represents the probability of success for independent Binomial distributions. Only one of logits or probs should be passed in.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Blockwise(*args, **kwargs)¶

Create a random variable for Blockwise.

See Blockwise for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the Blockwise distribution.

Parameters:

distributions – Python list of tfp.distributions.Distribution instances. All distribution instances must have the same batch_shape and all must have event_ndims==1, i.e., be vector-variate distributions.
dtype_override – samples of distributions will be cast to this dtype. If unspecified, all distributions must have the same dtype. Default value: None (i.e., do not cast).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Categorical(*args, **kwargs)¶

Create a random variable for Categorical.

See Categorical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize Categorical distributions using class log-probabilities.

Parameters:

logits – An N-D Tensor, N >= 1, representing the log probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each class. Only one of logits or probs should be passed in.
probs – An N-D Tensor, N >= 1, representing the probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each class. Only one of logits or probs should be passed in.
dtype – The type of the event samples (default: int32).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Cauchy(*args, **kwargs)¶

Create a random variable for Cauchy.

See Cauchy for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Cauchy distributions.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g. loc + scale is a valid operation).

Parameters:

loc – Floating point tensor; the modes of the distribution(s).
scale – Floating point tensor; the locations of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and scale have different dtype.

inferpy.models.random_variable.Chi(*args, **kwargs)¶

Create a random variable for Chi.

See Chi for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Chi distributions with parameter df.

Parameters:

df – Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class. Default value: ‘Chi’.

inferpy.models.random_variable.Chi2(*args, **kwargs)¶

Create a random variable for Chi2.

See Chi2 for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Chi2 distributions with parameter df.

Parameters:

df – Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Chi2WithAbsDf(*args, **kwargs)¶

Create a random variable for Chi2WithAbsDf.

See Chi2WithAbsDf for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2019-06-05. Instructions for updating: Chi2WithAbsDf is deprecated, use Chi2(df=tf.floor(tf.abs(df))) instead.

inferpy.models.random_variable.ConditionalDistribution(*args, **kwargs)¶

Create a random variable for ConditionalDistribution.

See ConditionalDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the Distribution.

This is a private method for subclass use.

Parameters:

dtype – The type of the event samples. None implies no type-enforcement.
reparameterization_type – Instance of ReparameterizationType. If tfd.FULLY_REPARAMETERIZED, this Distribution can be reparameterized in terms of some standard distribution with a function whose Jacobian is constant for the support of the standard distribution. If tfd.NOT_REPARAMETERIZED, then no such reparameterization is available.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
parameters – Python dict of parameters used to instantiate this Distribution.
graph_parents – Python list of graph prerequisites of this Distribution.
name – Python str name prefixed to Ops created by this class. Default: subclass name.

Raises:

ValueError – if any member of graph_parents is None or not a Tensor.

inferpy.models.random_variable.ConditionalTransformedDistribution(*args, **kwargs)¶

Create a random variable for ConditionalTransformedDistribution.

See ConditionalTransformedDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Transformed Distribution.

Parameters:

distribution – The base distribution instance to transform. Typically an instance of Distribution.
bijector – The object responsible for calculating the transformation. Typically an instance of Bijector.
batch_shape – integer vector Tensor which overrides distribution batch_shape; valid only if distribution.is_scalar_batch().
event_shape – integer vector Tensor which overrides distribution event_shape; valid only if distribution.is_scalar_event().
kwargs_split_fn –
Python callable which takes a kwargs dict and returns a tuple of kwargs dict`s for each of the `distribution and bijector parameters respectively. Default value: _default_kwargs_split_fn (i.e.,

`lambda kwargs: (kwargs.get(‘distribution_kwargs’, {}),
kwargs.get(‘bijector_kwargs’, {}))`)
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
parameters – Locals dict captured by subclass constructor, to be used for copy/slice re-instantiation operations.
name – Python str name prefixed to Ops created by this class. Default: bijector.name + distribution.name.

inferpy.models.random_variable.Deterministic(*args, **kwargs)¶

Create a random variable for Deterministic.

See Deterministic for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a scalar Deterministic distribution.

The atol and rtol parameters allow for some slack in pmf, cdf computations, e.g. due to floating-point error.

``` pmf(x; loc)

```

Parameters:

loc – Numeric Tensor of shape [B1, …, Bb], with b >= 0. The point (or batch of points) on which this distribution is supported.
atol – Non-negative Tensor of same dtype as loc and broadcastable shape. The absolute tolerance for comparing closeness to loc. Default is 0.
rtol – Non-negative Tensor of same dtype as loc and broadcastable shape. The relative tolerance for comparing closeness to loc. Default is 0.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Dirichlet(*args, **kwargs)¶

Create a random variable for Dirichlet.

See Dirichlet for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Dirichlet distributions.

Parameters:

concentration – Positive floating-point Tensor indicating mean number of class occurrences; aka “alpha”. Implies self.dtype, and self.batch_shape, self.event_shape, i.e., if concentration.shape = [N1, N2, …, Nm, k] then batch_shape = [N1, N2, …, Nm] and event_shape = [k].
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.DirichletMultinomial(*args, **kwargs)¶

Create a random variable for DirichletMultinomial.

See DirichletMultinomial for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of DirichletMultinomial distributions.

Parameters:

total_count – Non-negative floating point tensor, whose dtype is the same as concentration. The shape is broadcastable to [N1,…, Nm] with m >= 0. Defines this as a batch of N1 x … x Nm different Dirichlet multinomial distributions. Its components should be equal to integer values.
concentration – Positive floating point tensor, whose dtype is the same as n with shape broadcastable to [N1,…, Nm, K] m >= 0. Defines this as a batch of N1 x … x Nm different K class Dirichlet multinomial distributions.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Distribution(*args, **kwargs)¶

Create a random variable for Distribution.

See Distribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the Distribution.

This is a private method for subclass use.

Parameters:

dtype – The type of the event samples. None implies no type-enforcement.
reparameterization_type – Instance of ReparameterizationType. If tfd.FULLY_REPARAMETERIZED, this Distribution can be reparameterized in terms of some standard distribution with a function whose Jacobian is constant for the support of the standard distribution. If tfd.NOT_REPARAMETERIZED, then no such reparameterization is available.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
parameters – Python dict of parameters used to instantiate this Distribution.
graph_parents – Python list of graph prerequisites of this Distribution.
name – Python str name prefixed to Ops created by this class. Default: subclass name.

Raises:

ValueError – if any member of graph_parents is None or not a Tensor.

inferpy.models.random_variable.Empirical(*args, **kwargs)¶

Create a random variable for Empirical.

See Empirical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize Empirical distributions.

Parameters:

samples – Numeric Tensor of shape [B1, …, Bk, S, E1, …, En]`, k, n >= 0. Samples or batches of samples on which the distribution is based. The first k dimensions index into a batch of independent distributions. Length of S dimension determines number of samples in each multiset. The last n dimension represents samples for each distribution. n is specified by argument event_ndims.
event_ndims – Python int32, default 0. number of dimensions for each event. When 0 this distribution has scalar samples. When 1 this distribution has vector-like samples.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if the rank of samples < event_ndims + 1.

inferpy.models.random_variable.ExpRelaxedOneHotCategorical(*args, **kwargs)¶

Create a random variable for ExpRelaxedOneHotCategorical.

See ExpRelaxedOneHotCategorical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize ExpRelaxedOneHotCategorical using class log-probabilities.

Parameters:

temperature – An 0-D Tensor, representing the temperature of a set of ExpRelaxedCategorical distributions. The temperature should be positive.
logits – An N-D Tensor, N >= 1, representing the log probabilities of a set of ExpRelaxedCategorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each class. Only one of logits or probs should be passed in.
probs – An N-D Tensor, N >= 1, representing the probabilities of a set of ExpRelaxedCategorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each class. Only one of logits or probs should be passed in.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Exponential(*args, **kwargs)¶

Create a random variable for Exponential.

See Exponential for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Exponential distribution with parameter rate.

Parameters:

rate – Floating point tensor, equivalent to 1 / mean. Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.FiniteDiscrete(*args, **kwargs)¶

Create a random variable for FiniteDiscrete.

See FiniteDiscrete for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a finite discrete contribution.

Parameters:

outcomes – A 1-D floating or integer Tensor, representing a list of possible outcomes in strictly ascending order.
logits – A floating N-D Tensor, N >= 1, representing the log probabilities of a set of FiniteDiscrete distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each discrete value. Only one of logits or probs should be passed in.
probs – A floating N-D Tensor, N >= 1, representing the probabilities of a set of FiniteDiscrete distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each discrete value. Only one of logits or probs should be passed in.
rtol – Tensor with same dtype as outcomes. The relative tolerance for floating number comparison. Only effective when outcomes is a floating Tensor. Default is 10 * eps.
atol – Tensor with same dtype as outcomes. The absolute tolerance for floating number comparison. Only effective when outcomes is a floating Tensor. Default is 10 * eps.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value ‘NaN’ to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Gamma(*args, **kwargs)¶

Create a random variable for Gamma.

See Gamma for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Gamma with concentration and rate parameters.

The parameters concentration and rate must be shaped in a way that supports broadcasting (e.g. concentration + rate is a valid operation).

Parameters:

concentration – Floating point tensor, the concentration params of the distribution(s). Must contain only positive values.
rate – Floating point tensor, the inverse scale params of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if concentration and rate are different dtypes.

inferpy.models.random_variable.GammaGamma(*args, **kwargs)¶

Create a random variable for GammaGamma.

See GammaGamma for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initializes a batch of Gamma-Gamma distributions.

The parameters concentration and rate must be shaped in a way that supports broadcasting (e.g. concentration + mixing_concentration + mixing_rate is a valid operation).

Parameters:

concentration – Floating point tensor, the concentration params of the distribution(s). Must contain only positive values.
mixing_concentration – Floating point tensor, the concentration params of the mixing Gamma distribution(s). Must contain only positive values.
mixing_rate – Floating point tensor, the rate params of the mixing Gamma distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if concentration and rate are different dtypes.

inferpy.models.random_variable.GaussianProcess(*args, **kwargs)¶

Create a random variable for GaussianProcess.

See GaussianProcess for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Instantiate a GaussianProcess Distribution.

Parameters:

kernel – PositiveSemidefiniteKernel-like instance representing the GP’s covariance function.
index_points – float Tensor representing finite (batch of) vector(s) of points in the index set over which the GP is defined. Shape has the form [b1, …, bB, e, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e is the number (size) of index points in each batch. Ultimately this distribution corresponds to a e-dimensional multivariate normal. The batch shape must be broadcastable with kernel.batch_shape and any batch dims yielded by mean_fn.
mean_fn – Python callable that acts on index_points to produce a (batch of) vector(s) of mean values at index_points. Takes a Tensor of shape [b1, …, bB, f1, …, fF] and returns a Tensor whose shape is broadcastable with [b1, …, bB]. Default value: None implies constant zero function.
observation_noise_variance – float Tensor representing the variance of the noise in the Normal likelihood distribution of the model. May be batched, in which case the batch shape must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc.). Default value: 0.
jitter – float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: “GaussianProcess”.

Raises:

ValueError – if mean_fn is not None and is not callable.

inferpy.models.random_variable.GaussianProcessRegressionModel(*args, **kwargs)¶

Create a random variable for GaussianProcessRegressionModel.

See GaussianProcessRegressionModel for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a GaussianProcessRegressionModel instance.

Parameters:

kernel – PositiveSemidefiniteKernel-like instance representing the GP’s covariance function.
index_points – float Tensor representing finite collection, or batch of collections, of points in the index set over which the GP is defined. Shape has the form [b1, …, bB, e, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e is the number (size) of index points in each batch. Ultimately this distribution corresponds to an e-dimensional multivariate normal. The batch shape must be broadcastable with kernel.batch_shape and any batch dims yielded by mean_fn.
observation_index_points – float Tensor representing finite collection, or batch of collections, of points in the index set for which some data has been observed. Shape has the form [b1, …, bB, e, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims, and e is the number (size) of index points in each batch. [b1, …, bB, e] must be broadcastable with the shape of observations, and [b1, …, bB] must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc). The default value is None, which corresponds to the empty set of observations, and simply results in the prior predictive model (a GP with noise of variance predictive_noise_variance).
observations – float Tensor representing collection, or batch of collections, of observations corresponding to observation_index_points. Shape has the form [b1, …, bB, e], which must be brodcastable with the batch and example shapes of observation_index_points. The batch shape [b1, …, bB] must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc.). The default value is None, which corresponds to the empty set of observations, and simply results in the prior predictive model (a GP with noise of variance predictive_noise_variance).
observation_noise_variance – float Tensor representing the variance of the noise in the Normal likelihood distribution of the model. May be batched, in which case the batch shape must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc.). Default value: 0.
predictive_noise_variance – float Tensor representing the variance in the posterior predictive model. If None, we simply re-use observation_noise_variance for the posterior predictive noise. If set explicitly, however, we use this value. This allows us, for example, to omit predictive noise variance (by setting this to zero) to obtain noiseless posterior predictions of function values, conditioned on noisy observations.
mean_fn – Python callable that acts on index_points to produce a collection, or batch of collections, of mean values at index_points. Takes a Tensor of shape [b1, …, bB, f1, …, fF] and returns a Tensor whose shape is broadcastable with [b1, …, bB]. Default value: None implies the constant zero function.
jitter – float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: ‘GaussianProcessRegressionModel’.

Raises:

ValueError – if either - only one of observations and observation_index_points is given, or - mean_fn is not None and not callable.

inferpy.models.random_variable.Geometric(*args, **kwargs)¶

Create a random variable for Geometric.

See Geometric for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Geometric distributions.

Parameters:

logits – Floating-point Tensor with shape [B1, …, Bb] where b >= 0 indicates the number of batch dimensions. Each entry represents logits for the probability of success for independent Geometric distributions and must be in the range (-inf, inf]. Only one of logits or probs should be specified.
probs – Positive floating-point Tensor with shape [B1, …, Bb] where b >= 0 indicates the number of batch dimensions. Each entry represents the probability of success for independent Geometric distributions and must be in the range (0, 1]. Only one of logits or probs should be specified.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Gumbel(*args, **kwargs)¶

Create a random variable for Gumbel.

See Gumbel for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Gumbel distributions with location and scale loc and scale.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g. loc + scale is a valid operation).

Parameters:

loc – Floating point tensor, the means of the distribution(s).
scale – Floating point tensor, the scales of the distribution(s). scale must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘Gumbel’.

Raises:

TypeError – if loc and scale are different dtypes.

inferpy.models.random_variable.HalfCauchy(*args, **kwargs)¶

Create a random variable for HalfCauchy.

See HalfCauchy for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a half-Cauchy distribution with loc and scale.

Parameters:

loc – Floating-point Tensor; the location(s) of the distribution(s).
scale – Floating-point Tensor; the scale(s) of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False (i.e. do not validate args).
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘HalfCauchy’.

Raises:

TypeError – if loc and scale have different dtype.

inferpy.models.random_variable.HalfNormal(*args, **kwargs)¶

Create a random variable for HalfNormal.

See HalfNormal for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct HalfNormals with scale scale.

Parameters:

scale – Floating point tensor; the scales of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.HiddenMarkovModel(*args, **kwargs)¶

Create a random variable for HiddenMarkovModel.

See HiddenMarkovModel for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize hidden Markov model.

Parameters:

initial_distribution – A Categorical-like instance. Determines probability of first hidden state in Markov chain. The number of categories must match the number of categories of transition_distribution as well as both the rightmost batch dimension of transition_distribution and the rightmost batch dimension of observation_distribution.
transition_distribution – A Categorical-like instance. The rightmost batch dimension indexes the probability distribution of each hidden state conditioned on the previous hidden state.
observation_distribution – A tfp.distributions.Distribution-like instance. The rightmost batch dimension indexes the distribution of each observation conditioned on the corresponding hidden state.
num_steps – The number of steps taken in Markov chain. A python int.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: “HiddenMarkovModel”.

Raises:

ValueError – if num_steps is not at least 1.
ValueError – if initial_distribution does not have scalar event_shape.
ValueError – if transition_distribution does not have scalar event_shape.
ValueError – if transition_distribution and observation_distribution are fully defined but don’t have matching rightmost dimension.

inferpy.models.random_variable.Horseshoe(*args, **kwargs)¶

Create a random variable for Horseshoe.

See Horseshoe for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Horseshoe distribution with scale.

Parameters:

scale – Floating point tensor; the scales of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False (i.e., do not validate args).
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘Horseshoe’.

inferpy.models.random_variable.Independent(*args, **kwargs)¶

Create a random variable for Independent.

See Independent for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Independent distribution.

Parameters:

distribution – The base distribution instance to transform. Typically an instance of Distribution.
reinterpreted_batch_ndims – Scalar, integer number of rightmost batch dims which will be regarded as event dims. When None all but the first batch axis (batch axis 0) will be transferred to event dimensions (analogous to tf.layers.flatten).
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
name – The name for ops managed by the distribution. Default value: Independent + distribution.name.

Raises:

ValueError – if reinterpreted_batch_ndims exceeds distribution.batch_ndims

inferpy.models.random_variable.InverseGamma(*args, **kwargs)¶

Create a random variable for InverseGamma.

See InverseGamma for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct InverseGamma with concentration and scale parameters. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (rate). They will be removed after 2019-05-08. Instructions for updating: The rate parameter is deprecated. Use scale instead.The rate parameter was always interpreted as a scale parameter, but erroneously misnamed.

The parameters concentration and scale must be shaped in a way that supports broadcasting (e.g. concentration + scale is a valid operation).

Parameters:

concentration – Floating point tensor, the concentration params of the distribution(s). Must contain only positive values.
scale – Floating point tensor, the scale params of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
rate – Deprecated (mis-named) alias for scale.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if concentration and scale are different dtypes.

inferpy.models.random_variable.InverseGaussian(*args, **kwargs)¶

Create a random variable for InverseGaussian.

See InverseGaussian for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs inverse Gaussian distribution with loc and concentration.

Parameters:

loc – Floating-point Tensor, the loc params. Must contain only positive values.
concentration – Floating-point Tensor, the concentration params. Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False (i.e. do not validate args).
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘InverseGaussian’.

inferpy.models.random_variable.JointDistribution(*args, **kwargs)¶

Create a random variable for JointDistribution.

See JointDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the Distribution.

This is a private method for subclass use.

Parameters:

dtype – The type of the event samples. None implies no type-enforcement.
reparameterization_type – Instance of ReparameterizationType. If tfd.FULLY_REPARAMETERIZED, this Distribution can be reparameterized in terms of some standard distribution with a function whose Jacobian is constant for the support of the standard distribution. If tfd.NOT_REPARAMETERIZED, then no such reparameterization is available.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
parameters – Python dict of parameters used to instantiate this Distribution.
graph_parents – Python list of graph prerequisites of this Distribution.
name – Python str name prefixed to Ops created by this class. Default: subclass name.

Raises:

ValueError – if any member of graph_parents is None or not a Tensor.

inferpy.models.random_variable.JointDistributionCoroutine(*args, **kwargs)¶

Create a random variable for JointDistributionCoroutine.

See JointDistributionCoroutine for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the JointDistributionCoroutine distribution.

Parameters:

model – A generator that yields a sequence of tfd.Distribution-like instances.
sample_dtype – Samples from this distribution will be structured like tf.nest.pack_sequence_as(sample_dtype, list_). sample_dtype is only used for tf.nest.pack_sequence_as structuring of outputs, never casting (which is the responsibility of the component distributions). Default value: None (i.e., tuple).
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed. Default value: False.
name – The name for ops managed by the distribution. Default value: None (i.e., “JointDistributionCoroutine”).

inferpy.models.random_variable.JointDistributionNamed(*args, **kwargs)¶

Create a random variable for JointDistributionNamed.

See JointDistributionNamed for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the JointDistributionNamed distribution.

Parameters:

model – Python dict or namedtuple of distribution-making functions each with required args corresponding only to other keys.
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed. Default value: False.
name – The name for ops managed by the distribution. Default value: None (i.e., “JointDistributionNamed”).

inferpy.models.random_variable.JointDistributionSequential(*args, **kwargs)¶

Create a random variable for JointDistributionSequential.

See JointDistributionSequential for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the JointDistributionSequential distribution.

Parameters:

model – Python list of either tfd.Distribution instances and/or lambda functions which take the k previous distributions and returns a new tfd.Distribution instance.
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed. Default value: False.
name – The name for ops managed by the distribution. Default value: None (i.e., “JointDistributionSequential”).

class inferpy.models.random_variable.Kind[source]¶

Bases: enum.IntEnum

An enumeration.

GLOBAL_HIDDEN = 0¶

GLOBAL_OBSERVED = 1¶

LOCAL_HIDDEN = 2¶

LOCAL_OBSERVED = 3¶

inferpy.models.random_variable.Kumaraswamy(*args, **kwargs)¶

Create a random variable for Kumaraswamy.

See Kumaraswamy for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Kumaraswamy distributions.

Parameters:

concentration1 – Positive floating-point Tensor indicating mean number of successes; aka “alpha”. Implies self.dtype and self.batch_shape, i.e., concentration1.shape = [N1, N2, …, Nm] = self.batch_shape.
concentration0 – Positive floating-point Tensor indicating mean number of failures; aka “beta”. Otherwise has same semantics as concentration1.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.LKJ(*args, **kwargs)¶

Create a random variable for LKJ.

See LKJ for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct LKJ distributions.

Parameters:

dimension – Python int. The dimension of the correlation matrices to sample.
concentration – float or double Tensor. The positive concentration parameter of the LKJ distributions. The pdf of a sample matrix X is proportional to det(X) ** (concentration - 1).
input_output_cholesky – Python bool. If True, functions whose input or output have the semantics of samples assume inputs are in Cholesky form and return outputs in Cholesky form. In particular, if this flag is True, input to log_prob is presumed of Cholesky form and output from sample is of Cholesky form. Setting this argument to True is purely a computational optimization and does not change the underlying distribution. Additionally, validation checks which are only defined on the multiplied-out form are omitted, even if validate_args is True. Default value: False (i.e., input/output does not have Cholesky semantics).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – If dimension is negative.

inferpy.models.random_variable.Laplace(*args, **kwargs)¶

Create a random variable for Laplace.

See Laplace for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Laplace distribution with parameters loc and scale.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g., loc / scale is a valid operation).

Parameters:

loc – Floating point tensor which characterizes the location (center) of the distribution.
scale – Positive floating point tensor which characterizes the spread of the distribution.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and scale are of different dtype.

inferpy.models.random_variable.LinearGaussianStateSpaceModel(*args, **kwargs)¶

Create a random variable for LinearGaussianStateSpaceModel.

See LinearGaussianStateSpaceModel for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a `LinearGaussianStateSpaceModel.

Parameters:

num_timesteps – Integer Tensor total number of timesteps.
transition_matrix – A transition operator, represented by a Tensor or LinearOperator of shape [latent_size, latent_size], or by a callable taking as argument a scalar integer Tensor t and returning a Tensor or LinearOperator representing the transition operator from latent state at time t to time t + 1.
transition_noise – An instance of tfd.MultivariateNormalLinearOperator with event shape [latent_size], representing the mean and covariance of the transition noise model, or a callable taking as argument a scalar integer Tensor t and returning such a distribution representing the noise in the transition from time t to time t + 1.
observation_matrix – An observation operator, represented by a Tensor or LinearOperator of shape [observation_size, latent_size], or by a callable taking as argument a scalar integer Tensor t and returning a timestep-specific Tensor or LinearOperator.
observation_noise – An instance of tfd.MultivariateNormalLinearOperator with event shape [observation_size], representing the mean and covariance of the observation noise model, or a callable taking as argument a scalar integer Tensor t and returning a timestep-specific noise model.
initial_state_prior – An instance of MultivariateNormalLinearOperator representing the prior distribution on latent states; must have event shape [latent_size].
initial_step – optional int specifying the time of the first modeled timestep. This is added as an offset when passing timesteps t to (optional) callables specifying timestep-specific transition and observation models.
validate_args – Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc…) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – The name to give Ops created by the initializer.

inferpy.models.random_variable.LogNormal(*args, **kwargs)¶

Create a random variable for LogNormal.

See LogNormal for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a log-normal distribution.

The LogNormal distribution models positive-valued random variables whose logarithm is normally distributed with mean loc and standard deviation scale. It is constructed as the exponential transformation of a Normal distribution.

Parameters:

loc – Floating-point Tensor; the means of the underlying Normal distribution(s).
scale – Floating-point Tensor; the stddevs of the underlying Normal distribution(s).
validate_args – Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc…) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – The name to give Ops created by the initializer.

inferpy.models.random_variable.Logistic(*args, **kwargs)¶

Create a random variable for Logistic.

See Logistic for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Logistic distributions with mean and scale loc and scale.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g. loc + scale is a valid operation).

Parameters:

loc – Floating point tensor, the means of the distribution(s).
scale – Floating point tensor, the scales of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – The name to give Ops created by the initializer.

Raises:

TypeError – if loc and scale are different dtypes.

inferpy.models.random_variable.MixtureGaussian(locs, scales, logits=None, probs=None, *args, **kwargs)[source]¶

inferpy.models.random_variable.Multinomial(*args, **kwargs)¶

Create a random variable for Multinomial.

See Multinomial for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Multinomial distributions.

Parameters:

total_count – Non-negative floating point tensor with shape broadcastable to [N1,…, Nm] with m >= 0. Defines this as a batch of N1 x … x Nm different Multinomial distributions. Its components should be equal to integer values.
logits – Floating point tensor representing unnormalized log-probabilities of a positive event with shape broadcastable to [N1,…, Nm, K] m >= 0, and the same dtype as total_count. Defines this as a batch of N1 x … x Nm different K class Multinomial distributions. Only one of logits or probs should be passed in.
probs – Positive floating point tensor with shape broadcastable to [N1,…, Nm, K] m >= 0 and same dtype as total_count. Defines this as a batch of N1 x … x Nm different K class Multinomial distributions. probs’s components in the last portion of its shape should sum to 1. Only one of logits or probs should be passed in.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.MultivariateNormalDiag(*args, **kwargs)¶

Create a random variable for MultivariateNormalDiag.

See MultivariateNormalDiag for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T. A (non-batch) scale matrix is:

`none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) `

where:

scale_diag.shape = [k], and,
scale_identity_multiplier.shape = [].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.random_variable.MultivariateNormalDiagPlusLowRank(*args, **kwargs)¶

Create a random variable for MultivariateNormalDiagPlusLowRank.

See MultivariateNormalDiagPlusLowRank for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T. A (non-batch) scale matrix is:

```none scale = diag(scale_diag + scale_identity_multiplier ones(k)) +

```

where:

scale_diag.shape = [k],
scale_identity_multiplier.shape = [],
scale_perturb_factor.shape = [k, r], typically k >> r, and,
scale_perturb_diag.shape = [r].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_perturb_factor – Floating-point Tensor representing a rank-r perturbation added to scale. May have shape [B1, …, Bb, k, r], b >= 0, and characterizes b-batches of rank-r updates to scale. When None, no rank-r update is added to scale.
scale_perturb_diag – Floating-point Tensor representing a diagonal matrix inside the rank-r perturbation added to scale. May have shape [B1, …, Bb, r], b >= 0, and characterizes b-batches of r x r diagonal matrices inside the perturbation added to scale. When None, an identity matrix is used inside the perturbation. Can only be specified if scale_perturb_factor is also specified.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.random_variable.MultivariateNormalDiagWithSoftplusScale(*args, **kwargs)¶

Create a random variable for MultivariateNormalDiagWithSoftplusScale.

See MultivariateNormalDiagWithSoftplusScale for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2019-06-05. Instructions for updating: MultivariateNormalDiagWithSoftplusScale is deprecated, use MultivariateNormalDiag(loc=loc, scale_diag=tf.nn.softplus(scale_diag)) instead.

inferpy.models.random_variable.MultivariateNormalFullCovariance(*args, **kwargs)¶

Create a random variable for MultivariateNormalFullCovariance.

See MultivariateNormalFullCovariance for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and covariance_matrix arguments.

The event_shape is given by last dimension of the matrix implied by covariance_matrix. The last dimension of loc (if provided) must broadcast with this.

A non-batch covariance_matrix matrix is a k x k symmetric positive definite matrix. In other words it is (real) symmetric with all eigenvalues strictly positive.

Additional leading dimensions (if any) will index batches.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
covariance_matrix – Floating-point, symmetric positive definite Tensor of same dtype as loc. The strict upper triangle of covariance_matrix is ignored, so if covariance_matrix is not symmetric no error will be raised (unless validate_args is True). covariance_matrix has shape [B1, …, Bb, k, k] where b >= 0 and k is the event size.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if neither loc nor covariance_matrix are specified.

inferpy.models.random_variable.MultivariateNormalLinearOperator(*args, **kwargs)¶

Create a random variable for MultivariateNormalLinearOperator.

See MultivariateNormalLinearOperator for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T.

Additional leading dimensions (if any) will index batches.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale – Instance of LinearOperator with same dtype as loc and shape [B1, …, Bb, k, k].
validate_args – Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc…) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – The name to give Ops created by the initializer.

Raises:

ValueError – if scale is unspecified.
TypeError – if not scale.dtype.is_floating

inferpy.models.random_variable.MultivariateNormalTriL(*args, **kwargs)¶

Create a random variable for MultivariateNormalTriL.

See MultivariateNormalTriL for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T. A (non-batch) scale matrix is:

`none scale = scale_tril `

where scale_tril is lower-triangular k x k matrix with non-zero diagonal, i.e., tf.diag_part(scale_tril) != 0.

Additional leading dimensions (if any) will index batches.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_tril – Floating-point, lower-triangular Tensor with non-zero diagonal elements. scale_tril has shape [B1, …, Bb, k, k] where b >= 0 and k is the event size.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if neither loc nor scale_tril are specified.

inferpy.models.random_variable.MultivariateStudentTLinearOperator(*args, **kwargs)¶

Create a random variable for MultivariateStudentTLinearOperator.

See MultivariateStudentTLinearOperator for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Student’s t-distribution on R^k.

The batch_shape is the broadcast shape between df, loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc must broadcast with this.

Additional leading dimensions (if any) will index batches.

Parameters:

df – A positive floating-point Tensor. Has shape [B1, …, Bb] where b >= 0.
loc – Floating-point Tensor. Has shape [B1, …, Bb, k] where k is the event size.
scale – Instance of LinearOperator with a floating dtype and shape [B1, …, Bb, k, k].
validate_args – Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/variance/etc…) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – The name to give Ops created by the initializer.

Raises:

TypeError – if not scale.dtype.is_floating.
ValueError – if not scale.is_positive_definite.

inferpy.models.random_variable.NegativeBinomial(*args, **kwargs)¶

Create a random variable for NegativeBinomial.

See NegativeBinomial for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct NegativeBinomial distributions.

Parameters:

total_count – Non-negative floating-point Tensor with shape broadcastable to [B1,…, Bb] with b >= 0 and the same dtype as probs or logits. Defines this as a batch of N1 x … x Nm different Negative Binomial distributions. In practice, this represents the number of negative Bernoulli trials to stop at (the total_count of failures), but this is still a valid distribution when total_count is a non-integer.
logits – Floating-point Tensor with shape broadcastable to [B1, …, Bb] where b >= 0 indicates the number of batch dimensions. Each entry represents logits for the probability of success for independent Negative Binomial distributions and must be in the open interval (-inf, inf). Only one of logits or probs should be specified.
probs – Positive floating-point Tensor with shape broadcastable to [B1, …, Bb] where b >= 0 indicates the number of batch dimensions. Each entry represents the probability of success for independent Negative Binomial distributions and must be in the open interval (0, 1). Only one of logits or probs should be specified.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Normal(*args, **kwargs)¶

Create a random variable for Normal.

See Normal for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Normal distributions with mean and stddev loc and scale.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g. loc + scale is a valid operation).

Parameters:

loc – Floating point tensor; the means of the distribution(s).
scale – Floating point tensor; the stddevs of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and scale have different dtype.

inferpy.models.random_variable.OneHotCategorical(*args, **kwargs)¶

Create a random variable for OneHotCategorical.

See OneHotCategorical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize OneHotCategorical distributions using class log-probabilities.

Parameters:

logits – An N-D Tensor, N >= 1, representing the log probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each class. Only one of logits or probs should be passed in.
probs – An N-D Tensor, N >= 1, representing the probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each class. Only one of logits or probs should be passed in.
dtype – The type of the event samples (default: int32).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Pareto(*args, **kwargs)¶

Create a random variable for Pareto.

See Pareto for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Pareto distribution with concentration and scale.

Parameters:

concentration – Floating point tensor. Must contain only positive values.
scale – Floating point tensor, equivalent to mode. scale also restricts the domain of this distribution to be in [scale, inf). Must contain only positive values. Default value: 1.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False (i.e. do not validate args).
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘Pareto’.

inferpy.models.random_variable.Poisson(*args, **kwargs)¶

Create a random variable for Poisson.

See Poisson for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Poisson distributions.

Parameters:

rate – Floating point tensor, the rate parameter. rate must be positive. Must specify exactly one of rate and log_rate.
log_rate – Floating point tensor, the log of the rate parameter. Must specify exactly one of rate and log_rate.
interpolate_nondiscrete – Python bool. When False, log_prob returns -inf (and prob returns 0) for non-integer inputs. When True, log_prob evaluates the continuous function k * log_rate - lgamma(k+1) - rate, which matches the Poisson pmf at integer arguments k (note that this function is not itself a normalized probability log-density). Default value: True.
validate_args – Python bool. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if none or both of rate, log_rate are specified.
TypeError – if rate is not a float-type.
TypeError – if log_rate is not a float-type.

inferpy.models.random_variable.PoissonLogNormalQuadratureCompound(*args, **kwargs)¶

Create a random variable for PoissonLogNormalQuadratureCompound.

See PoissonLogNormalQuadratureCompound for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the PoissonLogNormalQuadratureCompound`.

Note: probs returned by (optional) quadrature_fn are presumed to be either a length-quadrature_size vector or a batch of vectors in 1-to-1 correspondence with the returned grid. (I.e., broadcasting is only partially supported.)

Parameters:

loc – float-like (batch of) scalar Tensor; the location parameter of the LogNormal prior.
scale – float-like (batch of) scalar Tensor; the scale parameter of the LogNormal prior.
quadrature_size – Python int scalar representing the number of quadrature points.
quadrature_fn – Python callable taking loc, scale, quadrature_size, validate_args and returning tuple(grid, probs) representing the LogNormal grid and corresponding normalized weight. normalized) weight. Default value: quadrature_scheme_lognormal_quantiles.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if quadrature_grid and quadrature_probs have different base dtype.

inferpy.models.random_variable.QuantizedDistribution(*args, **kwargs)¶

Create a random variable for QuantizedDistribution.

See QuantizedDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Quantized Distribution representing Y = ceiling(X).

Some properties are inherited from the distribution defining X. Example: allow_nan_stats is determined for this QuantizedDistribution by reading the distribution.

Parameters:

distribution – The base distribution class to transform. Typically an instance of Distribution.
low – Tensor with same dtype as this distribution and shape able to be added to samples. Should be a whole number. Default None. If provided, base distribution’s prob should be defined at low.
high – Tensor with same dtype as this distribution and shape able to be added to samples. Should be a whole number. Default None. If provided, base distribution’s prob should be defined at high - 1. high must be strictly greater than low.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – If dist_cls is not a subclass of Distribution or continuous.
NotImplementedError – If the base distribution does not implement cdf.

class inferpy.models.random_variable.RandomVariable(var, name, is_datamodel, ed_cls, var_args, var_kwargs, sample_shape, is_observed, observed_value)[source]¶

Bases: object

Class for random variables. It encapsulates the Random Variable from edward2, and additional properties.

It creates a variable generator. It must be a function without parameters, that creates a new Random Variable from edward2. It is used to define edward2 models as functions. Also, it is useful to define models using the intercept function.
The first time the var property is used, it creates a var using the variable generator.

build_in_session(sess)[source]¶: Allow to build a copy of the random variable but running previously each parameter in the tf session. This way, it uses the value of each tf variable or placeholder as a tensor, not as a tf variable or placeholder. If this random variable is a ed random variable directly assigned to .var, we cannot re-create it. In this case, return self. :param sess: tf session used to run each parameter used to build this random variable. :returns: the random variable object

copy()[source]¶: Makes a of the current random variable where the distribution parameters are fixed. :return: new object of class RandomVariable

property type¶

inferpy.models.random_variable.RelaxedBernoulli(*args, **kwargs)¶

Create a random variable for RelaxedBernoulli.

See RelaxedBernoulli for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct RelaxedBernoulli distributions.

Parameters:

temperature – An 0-D Tensor, representing the temperature of a set of RelaxedBernoulli distributions. The temperature should be positive.
logits – An N-D Tensor representing the log-odds of a positive event. Each entry in the Tensor parametrizes an independent RelaxedBernoulli distribution where the probability of an event is sigmoid(logits). Only one of logits or probs should be passed in.
probs – An N-D Tensor representing the probability of a positive event. Each entry in the Tensor parameterizes an independent Bernoulli distribution. Only one of logits or probs should be passed in.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – If both probs and logits are passed, or if neither.

inferpy.models.random_variable.RelaxedOneHotCategorical(*args, **kwargs)¶

Create a random variable for RelaxedOneHotCategorical.

See RelaxedOneHotCategorical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize RelaxedOneHotCategorical using class log-probabilities.

Parameters:

temperature – An 0-D Tensor, representing the temperature of a set of RelaxedOneHotCategorical distributions. The temperature should be positive.
logits – An N-D Tensor, N >= 1, representing the log probabilities of a set of RelaxedOneHotCategorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each class. Only one of logits or probs should be passed in.
probs – An N-D Tensor, N >= 1, representing the probabilities of a set of RelaxedOneHotCategorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each class. Only one of logits or probs should be passed in.
validate_args – Unused in this distribution.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc…) is undefined for any batch member. If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – A name for this distribution (optional).

inferpy.models.random_variable.Sample(*args, **kwargs)¶

Create a random variable for Sample.

See Sample for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the Sample distribution.

Parameters:

distribution – The base distribution instance to transform. Typically an instance of Distribution.
sample_shape – int scalar or vector Tensor representing the shape of a single sample.
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
name – The name for ops managed by the distribution. Default value: None (i.e., ‘Sample’ + distribution.name).

inferpy.models.random_variable.SinhArcsinh(*args, **kwargs)¶

Create a random variable for SinhArcsinh.

See SinhArcsinh for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct SinhArcsinh distribution on (-inf, inf).

Arguments (loc, scale, skewness, tailweight) must have broadcastable shape (indexing batch dimensions). They must all have the same dtype.

Parameters:

loc – Floating-point Tensor.
scale – Tensor of same dtype as loc.
skewness – Skewness parameter. Default is 0.0 (no skew).
tailweight – Tailweight parameter. Default is 1.0 (unchanged tailweight)
distribution – tf.Distribution-like instance. Distribution that is transformed to produce this distribution. Default is tfd.Normal(0., 1.). Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a SinhArcsinh sample and distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.StudentT(*args, **kwargs)¶

Create a random variable for StudentT.

See StudentT for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Student’s t distributions.

The distributions have degree of freedom df, mean loc, and scale scale.

The parameters df, loc, and scale must be shaped in a way that supports broadcasting (e.g. df + loc + scale is a valid operation).

Parameters:

df – Floating-point Tensor. The degrees of freedom of the distribution(s). df must contain only positive values.
loc – Floating-point Tensor. The mean(s) of the distribution(s).
scale – Floating-point Tensor. The scaling factor(s) for the distribution(s). Note that scale is not technically the standard deviation of this distribution but has semantics more similar to standard deviation than variance.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and scale are different dtypes.

inferpy.models.random_variable.StudentTProcess(*args, **kwargs)¶

Create a random variable for StudentTProcess.

See StudentTProcess for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Instantiate a StudentTProcess Distribution.

Parameters:

df – Positive Floating-point Tensor representing the degrees of freedom. Must be greater than 2.
kernel – PositiveSemidefiniteKernel-like instance representing the TP’s covariance function.
index_points – float Tensor representing finite (batch of) vector(s) of points in the index set over which the TP is defined. Shape has the form [b1, …, bB, e, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e is the number (size) of index points in each batch. Ultimately this distribution corresponds to a e-dimensional multivariate Student’s T. The batch shape must be broadcastable with kernel.batch_shape and any batch dims yielded by mean_fn.
mean_fn – Python callable that acts on index_points to produce a (batch of) vector(s) of mean values at index_points. Takes a Tensor of shape [b1, …, bB, f1, …, fF] and returns a Tensor whose shape is broadcastable with [b1, …, bB]. Default value: None implies constant zero function.
jitter – float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: “StudentTProcess”.

Raises:

ValueError – if mean_fn is not None and is not callable.

inferpy.models.random_variable.TransformedDistribution(*args, **kwargs)¶

Create a random variable for TransformedDistribution.

See TransformedDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Transformed Distribution.

Parameters:

distribution – The base distribution instance to transform. Typically an instance of Distribution.
bijector – The object responsible for calculating the transformation. Typically an instance of Bijector.
batch_shape – integer vector Tensor which overrides distribution batch_shape; valid only if distribution.is_scalar_batch().
event_shape – integer vector Tensor which overrides distribution event_shape; valid only if distribution.is_scalar_event().
kwargs_split_fn –
Python callable which takes a kwargs dict and returns a tuple of kwargs dict`s for each of the `distribution and bijector parameters respectively. Default value: _default_kwargs_split_fn (i.e.,

`lambda kwargs: (kwargs.get(‘distribution_kwargs’, {}),
kwargs.get(‘bijector_kwargs’, {}))`)
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
parameters – Locals dict captured by subclass constructor, to be used for copy/slice re-instantiation operations.
name – Python str name prefixed to Ops created by this class. Default: bijector.name + distribution.name.

inferpy.models.random_variable.Triangular(*args, **kwargs)¶

Create a random variable for Triangular.

See Triangular for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Triangular distributions.

Parameters:

low – Floating point tensor, lower boundary of the output interval. Must have low < high. Default value: 0.
high – Floating point tensor, upper boundary of the output interval. Must have low < high. Default value: 1.
peak – Floating point tensor, mode of the output interval. Must have low <= peak and peak <= high. Default value: 0.5.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘Triangular’.

Raises:

InvalidArgumentError – if validate_args=True and one of the following is True: * low >= high. * peak > high. * low > peak.

inferpy.models.random_variable.TruncatedNormal(*args, **kwargs)¶

Create a random variable for TruncatedNormal.

See TruncatedNormal for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct TruncatedNormal.

All parameters of the distribution will be broadcast to the same shape, so the resulting distribution will have a batch_shape of the broadcast shape of all parameters.

Parameters:

loc – Floating point tensor; the mean of the normal distribution(s) ( note that the mean of the resulting distribution will be different since it is modified by the bounds).
scale – Floating point tensor; the std deviation of the normal distribution(s).
low – float Tensor representing lower bound of the distribution’s support. Must be such that low < high.
high – float Tensor representing upper bound of the distribution’s support. Must be such that low < high.
validate_args – Python bool, default False. When True distribution parameters are checked at run-time.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.Uniform(*args, **kwargs)¶

Create a random variable for Uniform.

See Uniform for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Uniform distributions.

Parameters:

low – Floating point tensor, lower boundary of the output interval. Must have low < high.
high – Floating point tensor, upper boundary of the output interval. Must have low < high.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

InvalidArgumentError – if low >= high and validate_args=False.

inferpy.models.random_variable.VariationalGaussianProcess(*args, **kwargs)¶

Create a random variable for VariationalGaussianProcess.

See VariationalGaussianProcess for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Instantiate a VariationalGaussianProcess Distribution.

Parameters:

kernel – PositiveSemidefiniteKernel-like instance representing the GP’s covariance function.
index_points – float Tensor representing finite (batch of) vector(s) of points in the index set over which the VGP is defined. Shape has the form [b1, …, bB, e1, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e1 is the number (size) of index points in each batch (we denote it e1 to distinguish it from the numer of inducing index points, denoted e2 below). Ultimately the VariationalGaussianProcess distribution corresponds to an e1-dimensional multivariate normal. The batch shape must be broadcastable with kernel.batch_shape, the batch shape of inducing_index_points, and any batch dims yielded by mean_fn.
inducing_index_points – float Tensor of locations of inducing points in the index set. Shape has the form [b1, …, bB, e2, f1, …, fF], just like index_points. The batch shape components needn’t be identical to those of index_points, but must be broadcast compatible with them.
variational_inducing_observations_loc – float Tensor; the mean of the (full-rank Gaussian) variational posterior over function values at the inducing points, conditional on observed data. Shape has the form [b1, …, bB, e2], where b1, …, bB is broadcast compatible with other parameters’ batch shapes, and e2 is the number of inducing points.
variational_inducing_observations_scale – float Tensor; the scale matrix of the (full-rank Gaussian) variational posterior over function values at the inducing points, conditional on observed data. Shape has the form [b1, …, bB, e2, e2], where b1, …, bB is broadcast compatible with other parameters and e2 is the number of inducing points.
mean_fn – Python callable that acts on index points to produce a (batch of) vector(s) of mean values at those index points. Takes a Tensor of shape [b1, …, bB, f1, …, fF] and returns a Tensor whose shape is (broadcastable with) [b1, …, bB]. Default value: None implies constant zero function.
observation_noise_variance – float Tensor representing the variance of the noise in the Normal likelihood distribution of the model. May be batched, in which case the batch shape must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc.). Default value: 0.
predictive_noise_variance – float Tensor representing additional variance in the posterior predictive model. If None, we simply re-use observation_noise_variance for the posterior predictive noise. If set explicitly, however, we use the given value. This allows us, for example, to omit predictive noise variance (by setting this to zero) to obtain noiseless posterior predictions of function values, conditioned on noisy observations.
jitter – float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: “VariationalGaussianProcess”.

Raises:

ValueError – if mean_fn is not None and is not callable.

inferpy.models.random_variable.VectorDeterministic(*args, **kwargs)¶

Create a random variable for VectorDeterministic.

See VectorDeterministic for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a VectorDeterministic distribution on R^k, for k >= 0.

Note that there is only one point in R^0, the “point” []. So if k = 0 then self.prob([]) == 1.

The atol and rtol parameters allow for some slack in pmf computations, e.g. due to floating-point error.

``` pmf(x; loc)

```

Parameters:

loc – Numeric Tensor of shape [B1, …, Bb, k], with b >= 0, k >= 0 The point (or batch of points) on which this distribution is supported.
atol – Non-negative Tensor of same dtype as loc and broadcastable shape. The absolute tolerance for comparing closeness to loc. Default is 0.
rtol – Non-negative Tensor of same dtype as loc and broadcastable shape. The relative tolerance for comparing closeness to loc. Default is 0.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.random_variable.VectorDiffeomixture(*args, **kwargs)¶

Create a random variable for VectorDiffeomixture.

See VectorDiffeomixture for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the VectorDiffeomixture on R^d.

The vector diffeomixture (VDM) approximates the compound distribution

`none p(x) = int p(x | z) p(z) dz, where z is in the K-simplex, and p(x | z) := p(x | loc=sum_k z[k] loc[k], scale=sum_k z[k] scale[k]) `

Parameters:

mix_loc – float-like Tensor with shape [b1, …, bB, K-1]. In terms of samples, larger mix_loc[…, k] ==> Z is more likely to put more weight on its kth component.
temperature – float-like Tensor. Broadcastable with mix_loc. In terms of samples, smaller temperature means one component is more likely to dominate. I.e., smaller temperature makes the VDM look more like a standard mixture of K components.
distribution – tfp.distributions.Distribution-like instance. Distribution from which d iid samples are used as input to the selected affine transformation. Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a VectorDiffeomixture sample and the distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
loc – Length-K list of float-type Tensor`s. The `k-th element represents the shift used for the k-th affine transformation. If the k-th item is None, loc is implicitly 0. When specified, must have shape [B1, …, Bb, d] where b >= 0 and d is the event size.
scale – Length-K list of LinearOperator`s. Each should be positive-definite and operate on a `d-dimensional vector space. The k-th element represents the scale used for the k-th affine transformation. LinearOperator`s must have shape `[B1, …, Bb, d, d], b >= 0, i.e., characterizes b-batches of d x d matrices
quadrature_size – Python int scalar representing number of quadrature points. Larger quadrature_size means q_N(x) better approximates p(x).
quadrature_fn – Python callable taking normal_loc, normal_scale, quadrature_size, validate_args and returning tuple(grid, probs) representing the SoftmaxNormal grid and corresponding normalized weight. normalized) weight. Default value: quadrature_scheme_softmaxnormal_quantiles.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if not scale or len(scale) < 2.
ValueError – if len(loc) != len(scale)
ValueError – if quadrature_grid_and_probs is not None and len(quadrature_grid_and_probs[0]) != len(quadrature_grid_and_probs[1])
ValueError – if validate_args and any not scale.is_positive_definite.
TypeError – if any scale.dtype != scale[0].dtype.
TypeError – if any loc.dtype != scale[0].dtype.
NotImplementedError – if len(scale) != 2.
ValueError – if not distribution.is_scalar_batch.
ValueError – if not distribution.is_scalar_event.

inferpy.models.random_variable.VectorExponentialDiag(*args, **kwargs)¶

Create a random variable for VectorExponentialDiag.

See VectorExponentialDiag for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Vector Exponential distribution supported on a subset of R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T.

`none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) `

where:

scale_diag.shape = [k], and,
scale_identity_multiplier.shape = [].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.random_variable.VectorLaplaceDiag(*args, **kwargs)¶

Create a random variable for VectorLaplaceDiag.

See VectorLaplaceDiag for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Vector Laplace distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = 2 * scale @ scale.T.

`none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) `

where:

scale_diag.shape = [k], and,
scale_identity_multiplier.shape = [].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.random_variable.VectorSinhArcsinhDiag(*args, **kwargs)¶

Create a random variable for VectorSinhArcsinhDiag.

See VectorSinhArcsinhDiag for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct VectorSinhArcsinhDiag distribution on R^k.

The arguments scale_diag and scale_identity_multiplier combine to define the diagonal scale referred to in this class docstring:

`none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) `

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this

Additional leading dimensions (if any) will index batches.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scale-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scale k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
skewness – Skewness parameter. floating-point Tensor with shape broadcastable with event_shape.
tailweight – Tailweight parameter. floating-point Tensor with shape broadcastable with event_shape.
distribution – tf.Distribution-like instance. Distribution from which k iid samples are used as input to transformation F. Default is tfd.Normal(loc=0., scale=1.). Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a VectorSinhArcsinhDiag sample and distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.random_variable.VonMises(*args, **kwargs)¶

Create a random variable for VonMises.

See VonMises for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct von Mises distributions with given location and concentration.

The parameters loc and concentration must be shaped in a way that supports broadcasting (e.g. loc + concentration is a valid operation).

Parameters:

loc – Floating point tensor, the circular means of the distribution(s).
concentration – Floating point tensor, the level of concentration of the distribution(s) around loc. Must take non-negative values. concentration = 0 defines a Uniform distribution, while concentration = +inf indicates a Deterministic distribution at loc.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and concentration are different dtypes.

inferpy.models.random_variable.VonMisesFisher(*args, **kwargs)¶

Create a random variable for VonMisesFisher.

See VonMisesFisher for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Creates a new VonMisesFisher instance.

Parameters:

mean_direction – Floating-point Tensor with shape [B1, … Bn, D]. A unit vector indicating the mode of the distribution, or the unit-normalized direction of the mean. (This is not in general the mean of the distribution; the mean is not generally in the support of the distribution.) NOTE: D is currently restricted to <= 5.
concentration – Floating-point Tensor having batch shape [B1, … Bn] broadcastable with mean_direction. The level of concentration of samples around the mean_direction. concentration=0 indicates a uniform distribution over the unit hypersphere, and concentration=+inf indicates a Deterministic distribution (delta function) at mean_direction.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – For known-bad arguments, i.e. unsupported event dimension.

inferpy.models.random_variable.Wishart(*args, **kwargs)¶

Create a random variable for Wishart.

See Wishart for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Wishart distributions.

Parameters:

df – float or double Tensor. Degrees of freedom, must be greater than or equal to dimension of the scale matrix.
scale – float or double Tensor. The symmetric positive definite scale matrix of the distribution. Exactly one of scale and ‘scale_tril` must be passed.
scale_tril – float or double Tensor. The Cholesky factorization of the symmetric positive definite scale matrix of the distribution. Exactly one of scale and ‘scale_tril` must be passed.
input_output_cholesky – Python bool. If True, functions whose input or output have the semantics of samples assume inputs are in Cholesky form and return outputs in Cholesky form. In particular, if this flag is True, input to log_prob is presumed of Cholesky form and output from sample, mean, and mode are of Cholesky form. Setting this argument to True is purely a computational optimization and does not change the underlying distribution; for instance, mean returns the Cholesky of the mean, not the mean of Cholesky factors. The variance and stddev methods are unaffected by this flag. Default value: False (i.e., input/output does not have Cholesky semantics).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if zero or both of ‘scale’ and ‘scale_tril’ are passed in.

inferpy.models.random_variable.Zipf(*args, **kwargs)¶

Create a random variable for Zipf.

See Zipf for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Zipf distributions.

Parameters:

power – Float like Tensor representing the power parameter. Must be strictly greater than 1.
dtype – The dtype of Tensor returned by sample. Default value: tf.int32.
interpolate_nondiscrete – Python bool. When False, log_prob returns -inf (and prob returns 0) for non-integer inputs. When True, log_prob evaluates the continuous function -power log(k) - log(zeta(power)) , which matches the Zipf pmf at integer arguments k (note that this function is not itself a normalized probability log-density). Default value: True.
sample_maximum_iterations – Maximum number of iterations of allowable iterations in sample. When validate_args=True, samples which fail to reach convergence (subject to this cap) are masked out with self.dtype.min or nan depending on self.dtype.is_integer. Default value: 100.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: ‘Zipf’.

Raises:

TypeError – if power is not float like.

Module contents¶

inferpy.models.datamodel(size=None)[source]¶: This context is used to declare a plateau model. Random Variables and Parameters will use a sample_shape defined by the argument size, or by the data_model.fit. If size is not specified, the default size 1, or the size specified by fit will be used.

class inferpy.models.Parameter(initial_value, name=None)[source]¶

Bases: object

Random Variable parameter which can be optimized by an inference mechanism.

inferpy.models.probmodel(builder)[source]¶: Decorator to create probabilistic models. The function decorated must be a function which declares the Random Variables in the model. It is not required that the function returns such variables (they are captured using ed.tape).

inferpy.models.Autoregressive(*args, **kwargs)¶

Create a random variable for Autoregressive.

See Autoregressive for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct an Autoregressive distribution.

Parameters:

distribution_fn – Python callable which constructs a tfd.Distribution-like instance from a Tensor (e.g., sample0). The function must respect the “autoregressive property”, i.e., there exists a permutation of event such that each coordinate is a diffeomorphic function of on preceding coordinates.
sample0 – Initial input to distribution_fn; used to build the distribution in __init__ which in turn specifies this distribution’s properties, e.g., event_shape, batch_shape, dtype. If unspecified, then distribution_fn should be default constructable.
num_steps – Number of times distribution_fn is composed from samples, e.g., num_steps=2 implies distribution_fn(distribution_fn(sample0).sample(n)).sample().
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class. Default value: “Autoregressive”.

Raises:

ValueError – if num_steps and num_elements(distribution_fn(sample0).event_shape) are both None.
ValueError – if num_steps < 1.

inferpy.models.BatchReshape(*args, **kwargs)¶

Create a random variable for BatchReshape.

See BatchReshape for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct BatchReshape distribution.

Parameters:

distribution – The base distribution instance to reshape. Typically an instance of Distribution.
batch_shape – Positive int-like vector-shaped Tensor representing the new shape of the batch dimensions. Up to one dimension may contain -1, meaning the remainder of the batch size.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – The name to give Ops created by the initializer. Default value: “BatchReshape” + distribution.name.

Raises:

ValueError – if batch_shape is not a vector.
ValueError – if batch_shape has non-positive elements.
ValueError – if batch_shape size is not the same as a distribution.batch_shape size.

inferpy.models.Bernoulli(*args, **kwargs)¶

Create a random variable for Bernoulli.

See Bernoulli for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Bernoulli distributions.

Parameters:

logits – An N-D Tensor representing the log-odds of a 1 event. Each entry in the Tensor parametrizes an independent Bernoulli distribution where the probability of an event is sigmoid(logits). Only one of logits or probs should be passed in.
probs – An N-D Tensor representing the probability of a 1 event. Each entry in the Tensor parameterizes an independent Bernoulli distribution. Only one of logits or probs should be passed in.
dtype – The type of the event samples. Default: int32.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – If p and logits are passed, or if neither are passed.

inferpy.models.Beta(*args, **kwargs)¶

Create a random variable for Beta.

See Beta for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Beta distributions.

Parameters:

concentration1 – Positive floating-point Tensor indicating mean number of successes; aka “alpha”. Implies self.dtype and self.batch_shape, i.e., concentration1.shape = [N1, N2, …, Nm] = self.batch_shape.
concentration0 – Positive floating-point Tensor indicating mean number of failures; aka “beta”. Otherwise has same semantics as concentration1.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Binomial(*args, **kwargs)¶

Create a random variable for Binomial.

See Binomial for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Binomial distributions.

Parameters:

total_count – Non-negative floating point tensor with shape broadcastable to [N1,…, Nm] with m >= 0 and the same dtype as probs or logits. Defines this as a batch of N1 x … x Nm different Binomial distributions. Its components should be equal to integer values.
logits – Floating point tensor representing the log-odds of a positive event with shape broadcastable to [N1,…, Nm] m >= 0, and the same dtype as total_count. Each entry represents logits for the probability of success for independent Binomial distributions. Only one of logits or probs should be passed in.
probs – Positive floating point tensor with shape broadcastable to [N1,…, Nm] m >= 0, probs in [0, 1]. Each entry represents the probability of success for independent Binomial distributions. Only one of logits or probs should be passed in.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Blockwise(*args, **kwargs)¶

Create a random variable for Blockwise.

See Blockwise for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the Blockwise distribution.

Parameters:

distributions – Python list of tfp.distributions.Distribution instances. All distribution instances must have the same batch_shape and all must have event_ndims==1, i.e., be vector-variate distributions.
dtype_override – samples of distributions will be cast to this dtype. If unspecified, all distributions must have the same dtype. Default value: None (i.e., do not cast).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Categorical(*args, **kwargs)¶

Create a random variable for Categorical.

See Categorical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize Categorical distributions using class log-probabilities.

Parameters:

logits – An N-D Tensor, N >= 1, representing the log probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each class. Only one of logits or probs should be passed in.
probs – An N-D Tensor, N >= 1, representing the probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each class. Only one of logits or probs should be passed in.
dtype – The type of the event samples (default: int32).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Cauchy(*args, **kwargs)¶

Create a random variable for Cauchy.

See Cauchy for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Cauchy distributions.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g. loc + scale is a valid operation).

Parameters:

loc – Floating point tensor; the modes of the distribution(s).
scale – Floating point tensor; the locations of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and scale have different dtype.

inferpy.models.Chi(*args, **kwargs)¶

Create a random variable for Chi.

See Chi for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Chi distributions with parameter df.

Parameters:

df – Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class. Default value: ‘Chi’.

inferpy.models.Chi2(*args, **kwargs)¶

Create a random variable for Chi2.

See Chi2 for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Chi2 distributions with parameter df.

Parameters:

df – Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Chi2WithAbsDf(*args, **kwargs)¶

Create a random variable for Chi2WithAbsDf.

See Chi2WithAbsDf for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2019-06-05. Instructions for updating: Chi2WithAbsDf is deprecated, use Chi2(df=tf.floor(tf.abs(df))) instead.

inferpy.models.Deterministic(*args, **kwargs)¶

Create a random variable for Deterministic.

See Deterministic for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a scalar Deterministic distribution.

The atol and rtol parameters allow for some slack in pmf, cdf computations, e.g. due to floating-point error.

``` pmf(x; loc)

```

Parameters:

loc – Numeric Tensor of shape [B1, …, Bb], with b >= 0. The point (or batch of points) on which this distribution is supported.
atol – Non-negative Tensor of same dtype as loc and broadcastable shape. The absolute tolerance for comparing closeness to loc. Default is 0.
rtol – Non-negative Tensor of same dtype as loc and broadcastable shape. The relative tolerance for comparing closeness to loc. Default is 0.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.VectorDeterministic(*args, **kwargs)¶

Create a random variable for VectorDeterministic.

See VectorDeterministic for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a VectorDeterministic distribution on R^k, for k >= 0.

Note that there is only one point in R^0, the “point” []. So if k = 0 then self.prob([]) == 1.

The atol and rtol parameters allow for some slack in pmf computations, e.g. due to floating-point error.

``` pmf(x; loc)

```

Parameters:

loc – Numeric Tensor of shape [B1, …, Bb, k], with b >= 0, k >= 0 The point (or batch of points) on which this distribution is supported.
atol – Non-negative Tensor of same dtype as loc and broadcastable shape. The absolute tolerance for comparing closeness to loc. Default is 0.
rtol – Non-negative Tensor of same dtype as loc and broadcastable shape. The relative tolerance for comparing closeness to loc. Default is 0.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Dirichlet(*args, **kwargs)¶

Create a random variable for Dirichlet.

See Dirichlet for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Dirichlet distributions.

Parameters:

concentration – Positive floating-point Tensor indicating mean number of class occurrences; aka “alpha”. Implies self.dtype, and self.batch_shape, self.event_shape, i.e., if concentration.shape = [N1, N2, …, Nm, k] then batch_shape = [N1, N2, …, Nm] and event_shape = [k].
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.DirichletMultinomial(*args, **kwargs)¶

Create a random variable for DirichletMultinomial.

See DirichletMultinomial for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of DirichletMultinomial distributions.

Parameters:

total_count – Non-negative floating point tensor, whose dtype is the same as concentration. The shape is broadcastable to [N1,…, Nm] with m >= 0. Defines this as a batch of N1 x … x Nm different Dirichlet multinomial distributions. Its components should be equal to integer values.
concentration – Positive floating point tensor, whose dtype is the same as n with shape broadcastable to [N1,…, Nm, K] m >= 0. Defines this as a batch of N1 x … x Nm different K class Dirichlet multinomial distributions.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.ConditionalDistribution(*args, **kwargs)¶

Create a random variable for ConditionalDistribution.

See ConditionalDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the Distribution.

This is a private method for subclass use.

Parameters:

dtype – The type of the event samples. None implies no type-enforcement.
reparameterization_type – Instance of ReparameterizationType. If tfd.FULLY_REPARAMETERIZED, this Distribution can be reparameterized in terms of some standard distribution with a function whose Jacobian is constant for the support of the standard distribution. If tfd.NOT_REPARAMETERIZED, then no such reparameterization is available.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
parameters – Python dict of parameters used to instantiate this Distribution.
graph_parents – Python list of graph prerequisites of this Distribution.
name – Python str name prefixed to Ops created by this class. Default: subclass name.

Raises:

ValueError – if any member of graph_parents is None or not a Tensor.

inferpy.models.Distribution(*args, **kwargs)¶

Create a random variable for Distribution.

See Distribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the Distribution.

This is a private method for subclass use.

Parameters:

dtype – The type of the event samples. None implies no type-enforcement.
reparameterization_type – Instance of ReparameterizationType. If tfd.FULLY_REPARAMETERIZED, this Distribution can be reparameterized in terms of some standard distribution with a function whose Jacobian is constant for the support of the standard distribution. If tfd.NOT_REPARAMETERIZED, then no such reparameterization is available.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
parameters – Python dict of parameters used to instantiate this Distribution.
graph_parents – Python list of graph prerequisites of this Distribution.
name – Python str name prefixed to Ops created by this class. Default: subclass name.

Raises:

ValueError – if any member of graph_parents is None or not a Tensor.

inferpy.models.Empirical(*args, **kwargs)¶

Create a random variable for Empirical.

See Empirical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize Empirical distributions.

Parameters:

samples – Numeric Tensor of shape [B1, …, Bk, S, E1, …, En]`, k, n >= 0. Samples or batches of samples on which the distribution is based. The first k dimensions index into a batch of independent distributions. Length of S dimension determines number of samples in each multiset. The last n dimension represents samples for each distribution. n is specified by argument event_ndims.
event_ndims – Python int32, default 0. number of dimensions for each event. When 0 this distribution has scalar samples. When 1 this distribution has vector-like samples.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if the rank of samples < event_ndims + 1.

inferpy.models.Exponential(*args, **kwargs)¶

Create a random variable for Exponential.

See Exponential for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Exponential distribution with parameter rate.

Parameters:

rate – Floating point tensor, equivalent to 1 / mean. Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.FiniteDiscrete(*args, **kwargs)¶

Create a random variable for FiniteDiscrete.

See FiniteDiscrete for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a finite discrete contribution.

Parameters:

outcomes – A 1-D floating or integer Tensor, representing a list of possible outcomes in strictly ascending order.
logits – A floating N-D Tensor, N >= 1, representing the log probabilities of a set of FiniteDiscrete distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each discrete value. Only one of logits or probs should be passed in.
probs – A floating N-D Tensor, N >= 1, representing the probabilities of a set of FiniteDiscrete distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each discrete value. Only one of logits or probs should be passed in.
rtol – Tensor with same dtype as outcomes. The relative tolerance for floating number comparison. Only effective when outcomes is a floating Tensor. Default is 10 * eps.
atol – Tensor with same dtype as outcomes. The absolute tolerance for floating number comparison. Only effective when outcomes is a floating Tensor. Default is 10 * eps.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value ‘NaN’ to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Gamma(*args, **kwargs)¶

Create a random variable for Gamma.

See Gamma for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Gamma with concentration and rate parameters.

The parameters concentration and rate must be shaped in a way that supports broadcasting (e.g. concentration + rate is a valid operation).

Parameters:

concentration – Floating point tensor, the concentration params of the distribution(s). Must contain only positive values.
rate – Floating point tensor, the inverse scale params of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if concentration and rate are different dtypes.

inferpy.models.GammaGamma(*args, **kwargs)¶

Create a random variable for GammaGamma.

See GammaGamma for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initializes a batch of Gamma-Gamma distributions.

The parameters concentration and rate must be shaped in a way that supports broadcasting (e.g. concentration + mixing_concentration + mixing_rate is a valid operation).

Parameters:

concentration – Floating point tensor, the concentration params of the distribution(s). Must contain only positive values.
mixing_concentration – Floating point tensor, the concentration params of the mixing Gamma distribution(s). Must contain only positive values.
mixing_rate – Floating point tensor, the rate params of the mixing Gamma distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if concentration and rate are different dtypes.

inferpy.models.GaussianProcess(*args, **kwargs)¶

Create a random variable for GaussianProcess.

See GaussianProcess for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Instantiate a GaussianProcess Distribution.

Parameters:

kernel – PositiveSemidefiniteKernel-like instance representing the GP’s covariance function.
index_points – float Tensor representing finite (batch of) vector(s) of points in the index set over which the GP is defined. Shape has the form [b1, …, bB, e, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e is the number (size) of index points in each batch. Ultimately this distribution corresponds to a e-dimensional multivariate normal. The batch shape must be broadcastable with kernel.batch_shape and any batch dims yielded by mean_fn.
mean_fn – Python callable that acts on index_points to produce a (batch of) vector(s) of mean values at index_points. Takes a Tensor of shape [b1, …, bB, f1, …, fF] and returns a Tensor whose shape is broadcastable with [b1, …, bB]. Default value: None implies constant zero function.
observation_noise_variance – float Tensor representing the variance of the noise in the Normal likelihood distribution of the model. May be batched, in which case the batch shape must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc.). Default value: 0.
jitter – float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: “GaussianProcess”.

Raises:

ValueError – if mean_fn is not None and is not callable.

inferpy.models.GaussianProcessRegressionModel(*args, **kwargs)¶

Create a random variable for GaussianProcessRegressionModel.

See GaussianProcessRegressionModel for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a GaussianProcessRegressionModel instance.

Parameters:

kernel – PositiveSemidefiniteKernel-like instance representing the GP’s covariance function.
index_points – float Tensor representing finite collection, or batch of collections, of points in the index set over which the GP is defined. Shape has the form [b1, …, bB, e, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e is the number (size) of index points in each batch. Ultimately this distribution corresponds to an e-dimensional multivariate normal. The batch shape must be broadcastable with kernel.batch_shape and any batch dims yielded by mean_fn.
observation_index_points – float Tensor representing finite collection, or batch of collections, of points in the index set for which some data has been observed. Shape has the form [b1, …, bB, e, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims, and e is the number (size) of index points in each batch. [b1, …, bB, e] must be broadcastable with the shape of observations, and [b1, …, bB] must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc). The default value is None, which corresponds to the empty set of observations, and simply results in the prior predictive model (a GP with noise of variance predictive_noise_variance).
observations – float Tensor representing collection, or batch of collections, of observations corresponding to observation_index_points. Shape has the form [b1, …, bB, e], which must be brodcastable with the batch and example shapes of observation_index_points. The batch shape [b1, …, bB] must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc.). The default value is None, which corresponds to the empty set of observations, and simply results in the prior predictive model (a GP with noise of variance predictive_noise_variance).
observation_noise_variance – float Tensor representing the variance of the noise in the Normal likelihood distribution of the model. May be batched, in which case the batch shape must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc.). Default value: 0.
predictive_noise_variance – float Tensor representing the variance in the posterior predictive model. If None, we simply re-use observation_noise_variance for the posterior predictive noise. If set explicitly, however, we use this value. This allows us, for example, to omit predictive noise variance (by setting this to zero) to obtain noiseless posterior predictions of function values, conditioned on noisy observations.
mean_fn – Python callable that acts on index_points to produce a collection, or batch of collections, of mean values at index_points. Takes a Tensor of shape [b1, …, bB, f1, …, fF] and returns a Tensor whose shape is broadcastable with [b1, …, bB]. Default value: None implies the constant zero function.
jitter – float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: ‘GaussianProcessRegressionModel’.

Raises:

ValueError – if either - only one of observations and observation_index_points is given, or - mean_fn is not None and not callable.

inferpy.models.Geometric(*args, **kwargs)¶

Create a random variable for Geometric.

See Geometric for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Geometric distributions.

Parameters:

logits – Floating-point Tensor with shape [B1, …, Bb] where b >= 0 indicates the number of batch dimensions. Each entry represents logits for the probability of success for independent Geometric distributions and must be in the range (-inf, inf]. Only one of logits or probs should be specified.
probs – Positive floating-point Tensor with shape [B1, …, Bb] where b >= 0 indicates the number of batch dimensions. Each entry represents the probability of success for independent Geometric distributions and must be in the range (0, 1]. Only one of logits or probs should be specified.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Gumbel(*args, **kwargs)¶

Create a random variable for Gumbel.

See Gumbel for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Gumbel distributions with location and scale loc and scale.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g. loc + scale is a valid operation).

Parameters:

loc – Floating point tensor, the means of the distribution(s).
scale – Floating point tensor, the scales of the distribution(s). scale must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘Gumbel’.

Raises:

TypeError – if loc and scale are different dtypes.

inferpy.models.HalfCauchy(*args, **kwargs)¶

Create a random variable for HalfCauchy.

See HalfCauchy for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a half-Cauchy distribution with loc and scale.

Parameters:

loc – Floating-point Tensor; the location(s) of the distribution(s).
scale – Floating-point Tensor; the scale(s) of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False (i.e. do not validate args).
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘HalfCauchy’.

Raises:

TypeError – if loc and scale have different dtype.

inferpy.models.HalfNormal(*args, **kwargs)¶

Create a random variable for HalfNormal.

See HalfNormal for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct HalfNormals with scale scale.

Parameters:

scale – Floating point tensor; the scales of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.HiddenMarkovModel(*args, **kwargs)¶

Create a random variable for HiddenMarkovModel.

See HiddenMarkovModel for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize hidden Markov model.

Parameters:

initial_distribution – A Categorical-like instance. Determines probability of first hidden state in Markov chain. The number of categories must match the number of categories of transition_distribution as well as both the rightmost batch dimension of transition_distribution and the rightmost batch dimension of observation_distribution.
transition_distribution – A Categorical-like instance. The rightmost batch dimension indexes the probability distribution of each hidden state conditioned on the previous hidden state.
observation_distribution – A tfp.distributions.Distribution-like instance. The rightmost batch dimension indexes the distribution of each observation conditioned on the corresponding hidden state.
num_steps – The number of steps taken in Markov chain. A python int.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: “HiddenMarkovModel”.

Raises:

ValueError – if num_steps is not at least 1.
ValueError – if initial_distribution does not have scalar event_shape.
ValueError – if transition_distribution does not have scalar event_shape.
ValueError – if transition_distribution and observation_distribution are fully defined but don’t have matching rightmost dimension.

inferpy.models.Horseshoe(*args, **kwargs)¶

Create a random variable for Horseshoe.

See Horseshoe for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Horseshoe distribution with scale.

Parameters:

scale – Floating point tensor; the scales of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False (i.e., do not validate args).
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘Horseshoe’.

inferpy.models.Independent(*args, **kwargs)¶

Create a random variable for Independent.

See Independent for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Independent distribution.

Parameters:

distribution – The base distribution instance to transform. Typically an instance of Distribution.
reinterpreted_batch_ndims – Scalar, integer number of rightmost batch dims which will be regarded as event dims. When None all but the first batch axis (batch axis 0) will be transferred to event dimensions (analogous to tf.layers.flatten).
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
name – The name for ops managed by the distribution. Default value: Independent + distribution.name.

Raises:

ValueError – if reinterpreted_batch_ndims exceeds distribution.batch_ndims

inferpy.models.InverseGamma(*args, **kwargs)¶

Create a random variable for InverseGamma.

See InverseGamma for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct InverseGamma with concentration and scale parameters. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (rate). They will be removed after 2019-05-08. Instructions for updating: The rate parameter is deprecated. Use scale instead.The rate parameter was always interpreted as a scale parameter, but erroneously misnamed.

The parameters concentration and scale must be shaped in a way that supports broadcasting (e.g. concentration + scale is a valid operation).

Parameters:

concentration – Floating point tensor, the concentration params of the distribution(s). Must contain only positive values.
scale – Floating point tensor, the scale params of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
rate – Deprecated (mis-named) alias for scale.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if concentration and scale are different dtypes.

inferpy.models.InverseGaussian(*args, **kwargs)¶

Create a random variable for InverseGaussian.

See InverseGaussian for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs inverse Gaussian distribution with loc and concentration.

Parameters:

loc – Floating-point Tensor, the loc params. Must contain only positive values.
concentration – Floating-point Tensor, the concentration params. Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False (i.e. do not validate args).
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘InverseGaussian’.

inferpy.models.JointDistribution(*args, **kwargs)¶

Create a random variable for JointDistribution.

See JointDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the Distribution.

This is a private method for subclass use.

Parameters:

dtype – The type of the event samples. None implies no type-enforcement.
reparameterization_type – Instance of ReparameterizationType. If tfd.FULLY_REPARAMETERIZED, this Distribution can be reparameterized in terms of some standard distribution with a function whose Jacobian is constant for the support of the standard distribution. If tfd.NOT_REPARAMETERIZED, then no such reparameterization is available.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
parameters – Python dict of parameters used to instantiate this Distribution.
graph_parents – Python list of graph prerequisites of this Distribution.
name – Python str name prefixed to Ops created by this class. Default: subclass name.

Raises:

ValueError – if any member of graph_parents is None or not a Tensor.

inferpy.models.JointDistributionCoroutine(*args, **kwargs)¶

Create a random variable for JointDistributionCoroutine.

See JointDistributionCoroutine for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the JointDistributionCoroutine distribution.

Parameters:

model – A generator that yields a sequence of tfd.Distribution-like instances.
sample_dtype – Samples from this distribution will be structured like tf.nest.pack_sequence_as(sample_dtype, list_). sample_dtype is only used for tf.nest.pack_sequence_as structuring of outputs, never casting (which is the responsibility of the component distributions). Default value: None (i.e., tuple).
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed. Default value: False.
name – The name for ops managed by the distribution. Default value: None (i.e., “JointDistributionCoroutine”).

inferpy.models.JointDistributionNamed(*args, **kwargs)¶

Create a random variable for JointDistributionNamed.

See JointDistributionNamed for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the JointDistributionNamed distribution.

Parameters:

model – Python dict or namedtuple of distribution-making functions each with required args corresponding only to other keys.
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed. Default value: False.
name – The name for ops managed by the distribution. Default value: None (i.e., “JointDistributionNamed”).

inferpy.models.JointDistributionSequential(*args, **kwargs)¶

Create a random variable for JointDistributionSequential.

See JointDistributionSequential for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the JointDistributionSequential distribution.

Parameters:

model – Python list of either tfd.Distribution instances and/or lambda functions which take the k previous distributions and returns a new tfd.Distribution instance.
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed. Default value: False.
name – The name for ops managed by the distribution. Default value: None (i.e., “JointDistributionSequential”).

inferpy.models.Kumaraswamy(*args, **kwargs)¶

Create a random variable for Kumaraswamy.

See Kumaraswamy for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Kumaraswamy distributions.

Parameters:

concentration1 – Positive floating-point Tensor indicating mean number of successes; aka “alpha”. Implies self.dtype and self.batch_shape, i.e., concentration1.shape = [N1, N2, …, Nm] = self.batch_shape.
concentration0 – Positive floating-point Tensor indicating mean number of failures; aka “beta”. Otherwise has same semantics as concentration1.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Laplace(*args, **kwargs)¶

Create a random variable for Laplace.

See Laplace for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Laplace distribution with parameters loc and scale.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g., loc / scale is a valid operation).

Parameters:

loc – Floating point tensor which characterizes the location (center) of the distribution.
scale – Positive floating point tensor which characterizes the spread of the distribution.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and scale are of different dtype.

inferpy.models.LinearGaussianStateSpaceModel(*args, **kwargs)¶

Create a random variable for LinearGaussianStateSpaceModel.

See LinearGaussianStateSpaceModel for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a `LinearGaussianStateSpaceModel.

Parameters:

num_timesteps – Integer Tensor total number of timesteps.
transition_matrix – A transition operator, represented by a Tensor or LinearOperator of shape [latent_size, latent_size], or by a callable taking as argument a scalar integer Tensor t and returning a Tensor or LinearOperator representing the transition operator from latent state at time t to time t + 1.
transition_noise – An instance of tfd.MultivariateNormalLinearOperator with event shape [latent_size], representing the mean and covariance of the transition noise model, or a callable taking as argument a scalar integer Tensor t and returning such a distribution representing the noise in the transition from time t to time t + 1.
observation_matrix – An observation operator, represented by a Tensor or LinearOperator of shape [observation_size, latent_size], or by a callable taking as argument a scalar integer Tensor t and returning a timestep-specific Tensor or LinearOperator.
observation_noise – An instance of tfd.MultivariateNormalLinearOperator with event shape [observation_size], representing the mean and covariance of the observation noise model, or a callable taking as argument a scalar integer Tensor t and returning a timestep-specific noise model.
initial_state_prior – An instance of MultivariateNormalLinearOperator representing the prior distribution on latent states; must have event shape [latent_size].
initial_step – optional int specifying the time of the first modeled timestep. This is added as an offset when passing timesteps t to (optional) callables specifying timestep-specific transition and observation models.
validate_args – Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc…) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – The name to give Ops created by the initializer.

inferpy.models.LKJ(*args, **kwargs)¶

Create a random variable for LKJ.

See LKJ for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct LKJ distributions.

Parameters:

dimension – Python int. The dimension of the correlation matrices to sample.
concentration – float or double Tensor. The positive concentration parameter of the LKJ distributions. The pdf of a sample matrix X is proportional to det(X) ** (concentration - 1).
input_output_cholesky – Python bool. If True, functions whose input or output have the semantics of samples assume inputs are in Cholesky form and return outputs in Cholesky form. In particular, if this flag is True, input to log_prob is presumed of Cholesky form and output from sample is of Cholesky form. Setting this argument to True is purely a computational optimization and does not change the underlying distribution. Additionally, validation checks which are only defined on the multiplied-out form are omitted, even if validate_args is True. Default value: False (i.e., input/output does not have Cholesky semantics).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – If dimension is negative.

inferpy.models.Logistic(*args, **kwargs)¶

Create a random variable for Logistic.

See Logistic for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Logistic distributions with mean and scale loc and scale.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g. loc + scale is a valid operation).

Parameters:

loc – Floating point tensor, the means of the distribution(s).
scale – Floating point tensor, the scales of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – The name to give Ops created by the initializer.

Raises:

TypeError – if loc and scale are different dtypes.

inferpy.models.LogNormal(*args, **kwargs)¶

Create a random variable for LogNormal.

See LogNormal for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a log-normal distribution.

The LogNormal distribution models positive-valued random variables whose logarithm is normally distributed with mean loc and standard deviation scale. It is constructed as the exponential transformation of a Normal distribution.

Parameters:

loc – Floating-point Tensor; the means of the underlying Normal distribution(s).
scale – Floating-point Tensor; the stddevs of the underlying Normal distribution(s).
validate_args – Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc…) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – The name to give Ops created by the initializer.

inferpy.models.Multinomial(*args, **kwargs)¶

Create a random variable for Multinomial.

See Multinomial for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Multinomial distributions.

Parameters:

total_count – Non-negative floating point tensor with shape broadcastable to [N1,…, Nm] with m >= 0. Defines this as a batch of N1 x … x Nm different Multinomial distributions. Its components should be equal to integer values.
logits – Floating point tensor representing unnormalized log-probabilities of a positive event with shape broadcastable to [N1,…, Nm, K] m >= 0, and the same dtype as total_count. Defines this as a batch of N1 x … x Nm different K class Multinomial distributions. Only one of logits or probs should be passed in.
probs – Positive floating point tensor with shape broadcastable to [N1,…, Nm, K] m >= 0 and same dtype as total_count. Defines this as a batch of N1 x … x Nm different K class Multinomial distributions. probs’s components in the last portion of its shape should sum to 1. Only one of logits or probs should be passed in.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.MultivariateStudentTLinearOperator(*args, **kwargs)¶

Create a random variable for MultivariateStudentTLinearOperator.

See MultivariateStudentTLinearOperator for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Student’s t-distribution on R^k.

The batch_shape is the broadcast shape between df, loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc must broadcast with this.

Additional leading dimensions (if any) will index batches.

Parameters:

df – A positive floating-point Tensor. Has shape [B1, …, Bb] where b >= 0.
loc – Floating-point Tensor. Has shape [B1, …, Bb, k] where k is the event size.
scale – Instance of LinearOperator with a floating dtype and shape [B1, …, Bb, k, k].
validate_args – Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/variance/etc…) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – The name to give Ops created by the initializer.

Raises:

TypeError – if not scale.dtype.is_floating.
ValueError – if not scale.is_positive_definite.

inferpy.models.MultivariateNormalDiag(*args, **kwargs)¶

Create a random variable for MultivariateNormalDiag.

See MultivariateNormalDiag for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T. A (non-batch) scale matrix is:

`none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) `

where:

scale_diag.shape = [k], and,
scale_identity_multiplier.shape = [].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.MultivariateNormalDiagWithSoftplusScale(*args, **kwargs)¶

Create a random variable for MultivariateNormalDiagWithSoftplusScale.

See MultivariateNormalDiagWithSoftplusScale for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2019-06-05. Instructions for updating: MultivariateNormalDiagWithSoftplusScale is deprecated, use MultivariateNormalDiag(loc=loc, scale_diag=tf.nn.softplus(scale_diag)) instead.

inferpy.models.MultivariateNormalDiagPlusLowRank(*args, **kwargs)¶

Create a random variable for MultivariateNormalDiagPlusLowRank.

See MultivariateNormalDiagPlusLowRank for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T. A (non-batch) scale matrix is:

```none scale = diag(scale_diag + scale_identity_multiplier ones(k)) +

```

where:

scale_diag.shape = [k],
scale_identity_multiplier.shape = [],
scale_perturb_factor.shape = [k, r], typically k >> r, and,
scale_perturb_diag.shape = [r].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_perturb_factor – Floating-point Tensor representing a rank-r perturbation added to scale. May have shape [B1, …, Bb, k, r], b >= 0, and characterizes b-batches of rank-r updates to scale. When None, no rank-r update is added to scale.
scale_perturb_diag – Floating-point Tensor representing a diagonal matrix inside the rank-r perturbation added to scale. May have shape [B1, …, Bb, r], b >= 0, and characterizes b-batches of r x r diagonal matrices inside the perturbation added to scale. When None, an identity matrix is used inside the perturbation. Can only be specified if scale_perturb_factor is also specified.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.MultivariateNormalFullCovariance(*args, **kwargs)¶

Create a random variable for MultivariateNormalFullCovariance.

See MultivariateNormalFullCovariance for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and covariance_matrix arguments.

The event_shape is given by last dimension of the matrix implied by covariance_matrix. The last dimension of loc (if provided) must broadcast with this.

A non-batch covariance_matrix matrix is a k x k symmetric positive definite matrix. In other words it is (real) symmetric with all eigenvalues strictly positive.

Additional leading dimensions (if any) will index batches.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
covariance_matrix – Floating-point, symmetric positive definite Tensor of same dtype as loc. The strict upper triangle of covariance_matrix is ignored, so if covariance_matrix is not symmetric no error will be raised (unless validate_args is True). covariance_matrix has shape [B1, …, Bb, k, k] where b >= 0 and k is the event size.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if neither loc nor covariance_matrix are specified.

inferpy.models.MultivariateNormalLinearOperator(*args, **kwargs)¶

Create a random variable for MultivariateNormalLinearOperator.

See MultivariateNormalLinearOperator for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T.

Additional leading dimensions (if any) will index batches.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale – Instance of LinearOperator with same dtype as loc and shape [B1, …, Bb, k, k].
validate_args – Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc…) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – The name to give Ops created by the initializer.

Raises:

ValueError – if scale is unspecified.
TypeError – if not scale.dtype.is_floating

inferpy.models.MultivariateNormalTriL(*args, **kwargs)¶

Create a random variable for MultivariateNormalTriL.

See MultivariateNormalTriL for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T. A (non-batch) scale matrix is:

`none scale = scale_tril `

where scale_tril is lower-triangular k x k matrix with non-zero diagonal, i.e., tf.diag_part(scale_tril) != 0.

Additional leading dimensions (if any) will index batches.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_tril – Floating-point, lower-triangular Tensor with non-zero diagonal elements. scale_tril has shape [B1, …, Bb, k, k] where b >= 0 and k is the event size.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if neither loc nor scale_tril are specified.

inferpy.models.NegativeBinomial(*args, **kwargs)¶

Create a random variable for NegativeBinomial.

See NegativeBinomial for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct NegativeBinomial distributions.

Parameters:

total_count – Non-negative floating-point Tensor with shape broadcastable to [B1,…, Bb] with b >= 0 and the same dtype as probs or logits. Defines this as a batch of N1 x … x Nm different Negative Binomial distributions. In practice, this represents the number of negative Bernoulli trials to stop at (the total_count of failures), but this is still a valid distribution when total_count is a non-integer.
logits – Floating-point Tensor with shape broadcastable to [B1, …, Bb] where b >= 0 indicates the number of batch dimensions. Each entry represents logits for the probability of success for independent Negative Binomial distributions and must be in the open interval (-inf, inf). Only one of logits or probs should be specified.
probs – Positive floating-point Tensor with shape broadcastable to [B1, …, Bb] where b >= 0 indicates the number of batch dimensions. Each entry represents the probability of success for independent Negative Binomial distributions and must be in the open interval (0, 1). Only one of logits or probs should be specified.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Normal(*args, **kwargs)¶

Create a random variable for Normal.

See Normal for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Normal distributions with mean and stddev loc and scale.

The parameters loc and scale must be shaped in a way that supports broadcasting (e.g. loc + scale is a valid operation).

Parameters:

loc – Floating point tensor; the means of the distribution(s).
scale – Floating point tensor; the stddevs of the distribution(s). Must contain only positive values.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and scale have different dtype.

inferpy.models.OneHotCategorical(*args, **kwargs)¶

Create a random variable for OneHotCategorical.

See OneHotCategorical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize OneHotCategorical distributions using class log-probabilities.

Parameters:

logits – An N-D Tensor, N >= 1, representing the log probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each class. Only one of logits or probs should be passed in.
probs – An N-D Tensor, N >= 1, representing the probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each class. Only one of logits or probs should be passed in.
dtype – The type of the event samples (default: int32).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Pareto(*args, **kwargs)¶

Create a random variable for Pareto.

See Pareto for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Pareto distribution with concentration and scale.

Parameters:

concentration – Floating point tensor. Must contain only positive values.
scale – Floating point tensor, equivalent to mode. scale also restricts the domain of this distribution to be in [scale, inf). Must contain only positive values. Default value: 1.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False (i.e. do not validate args).
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘Pareto’.

inferpy.models.Poisson(*args, **kwargs)¶

Create a random variable for Poisson.

See Poisson for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Poisson distributions.

Parameters:

rate – Floating point tensor, the rate parameter. rate must be positive. Must specify exactly one of rate and log_rate.
log_rate – Floating point tensor, the log of the rate parameter. Must specify exactly one of rate and log_rate.
interpolate_nondiscrete – Python bool. When False, log_prob returns -inf (and prob returns 0) for non-integer inputs. When True, log_prob evaluates the continuous function k * log_rate - lgamma(k+1) - rate, which matches the Poisson pmf at integer arguments k (note that this function is not itself a normalized probability log-density). Default value: True.
validate_args – Python bool. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if none or both of rate, log_rate are specified.
TypeError – if rate is not a float-type.
TypeError – if log_rate is not a float-type.

inferpy.models.PoissonLogNormalQuadratureCompound(*args, **kwargs)¶

Create a random variable for PoissonLogNormalQuadratureCompound.

See PoissonLogNormalQuadratureCompound for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the PoissonLogNormalQuadratureCompound`.

Note: probs returned by (optional) quadrature_fn are presumed to be either a length-quadrature_size vector or a batch of vectors in 1-to-1 correspondence with the returned grid. (I.e., broadcasting is only partially supported.)

Parameters:

loc – float-like (batch of) scalar Tensor; the location parameter of the LogNormal prior.
scale – float-like (batch of) scalar Tensor; the scale parameter of the LogNormal prior.
quadrature_size – Python int scalar representing the number of quadrature points.
quadrature_fn – Python callable taking loc, scale, quadrature_size, validate_args and returning tuple(grid, probs) representing the LogNormal grid and corresponding normalized weight. normalized) weight. Default value: quadrature_scheme_lognormal_quantiles.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if quadrature_grid and quadrature_probs have different base dtype.

inferpy.models.QuantizedDistribution(*args, **kwargs)¶

Create a random variable for QuantizedDistribution.

See QuantizedDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Quantized Distribution representing Y = ceiling(X).

Some properties are inherited from the distribution defining X. Example: allow_nan_stats is determined for this QuantizedDistribution by reading the distribution.

Parameters:

distribution – The base distribution class to transform. Typically an instance of Distribution.
low – Tensor with same dtype as this distribution and shape able to be added to samples. Should be a whole number. Default None. If provided, base distribution’s prob should be defined at low.
high – Tensor with same dtype as this distribution and shape able to be added to samples. Should be a whole number. Default None. If provided, base distribution’s prob should be defined at high - 1. high must be strictly greater than low.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – If dist_cls is not a subclass of Distribution or continuous.
NotImplementedError – If the base distribution does not implement cdf.

inferpy.models.RelaxedBernoulli(*args, **kwargs)¶

Create a random variable for RelaxedBernoulli.

See RelaxedBernoulli for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct RelaxedBernoulli distributions.

Parameters:

temperature – An 0-D Tensor, representing the temperature of a set of RelaxedBernoulli distributions. The temperature should be positive.
logits – An N-D Tensor representing the log-odds of a positive event. Each entry in the Tensor parametrizes an independent RelaxedBernoulli distribution where the probability of an event is sigmoid(logits). Only one of logits or probs should be passed in.
probs – An N-D Tensor representing the probability of a positive event. Each entry in the Tensor parameterizes an independent Bernoulli distribution. Only one of logits or probs should be passed in.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – If both probs and logits are passed, or if neither.

inferpy.models.ExpRelaxedOneHotCategorical(*args, **kwargs)¶

Create a random variable for ExpRelaxedOneHotCategorical.

See ExpRelaxedOneHotCategorical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize ExpRelaxedOneHotCategorical using class log-probabilities.

Parameters:

temperature – An 0-D Tensor, representing the temperature of a set of ExpRelaxedCategorical distributions. The temperature should be positive.
logits – An N-D Tensor, N >= 1, representing the log probabilities of a set of ExpRelaxedCategorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each class. Only one of logits or probs should be passed in.
probs – An N-D Tensor, N >= 1, representing the probabilities of a set of ExpRelaxedCategorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each class. Only one of logits or probs should be passed in.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.RelaxedOneHotCategorical(*args, **kwargs)¶

Create a random variable for RelaxedOneHotCategorical.

See RelaxedOneHotCategorical for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize RelaxedOneHotCategorical using class log-probabilities.

Parameters:

temperature – An 0-D Tensor, representing the temperature of a set of RelaxedOneHotCategorical distributions. The temperature should be positive.
logits – An N-D Tensor, N >= 1, representing the log probabilities of a set of RelaxedOneHotCategorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of logits for each class. Only one of logits or probs should be passed in.
probs – An N-D Tensor, N >= 1, representing the probabilities of a set of RelaxedOneHotCategorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension represents a vector of probabilities for each class. Only one of logits or probs should be passed in.
validate_args – Unused in this distribution.
allow_nan_stats – Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc…) is undefined for any batch member. If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name – A name for this distribution (optional).

inferpy.models.Sample(*args, **kwargs)¶

Create a random variable for Sample.

See Sample for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct the Sample distribution.

Parameters:

distribution – The base distribution instance to transform. Typically an instance of Distribution.
sample_shape – int scalar or vector Tensor representing the shape of a single sample.
validate_args – Python bool. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
name – The name for ops managed by the distribution. Default value: None (i.e., ‘Sample’ + distribution.name).

inferpy.models.SinhArcsinh(*args, **kwargs)¶

Create a random variable for SinhArcsinh.

See SinhArcsinh for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct SinhArcsinh distribution on (-inf, inf).

Arguments (loc, scale, skewness, tailweight) must have broadcastable shape (indexing batch dimensions). They must all have the same dtype.

Parameters:

loc – Floating-point Tensor.
scale – Tensor of same dtype as loc.
skewness – Skewness parameter. Default is 0.0 (no skew).
tailweight – Tailweight parameter. Default is 1.0 (unchanged tailweight)
distribution – tf.Distribution-like instance. Distribution that is transformed to produce this distribution. Default is tfd.Normal(0., 1.). Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a SinhArcsinh sample and distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.StudentT(*args, **kwargs)¶

Create a random variable for StudentT.

See StudentT for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Student’s t distributions.

The distributions have degree of freedom df, mean loc, and scale scale.

The parameters df, loc, and scale must be shaped in a way that supports broadcasting (e.g. df + loc + scale is a valid operation).

Parameters:

df – Floating-point Tensor. The degrees of freedom of the distribution(s). df must contain only positive values.
loc – Floating-point Tensor. The mean(s) of the distribution(s).
scale – Floating-point Tensor. The scaling factor(s) for the distribution(s). Note that scale is not technically the standard deviation of this distribution but has semantics more similar to standard deviation than variance.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and scale are different dtypes.

inferpy.models.StudentTProcess(*args, **kwargs)¶

Create a random variable for StudentTProcess.

See StudentTProcess for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Instantiate a StudentTProcess Distribution.

Parameters:

df – Positive Floating-point Tensor representing the degrees of freedom. Must be greater than 2.
kernel – PositiveSemidefiniteKernel-like instance representing the TP’s covariance function.
index_points – float Tensor representing finite (batch of) vector(s) of points in the index set over which the TP is defined. Shape has the form [b1, …, bB, e, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e is the number (size) of index points in each batch. Ultimately this distribution corresponds to a e-dimensional multivariate Student’s T. The batch shape must be broadcastable with kernel.batch_shape and any batch dims yielded by mean_fn.
mean_fn – Python callable that acts on index_points to produce a (batch of) vector(s) of mean values at index_points. Takes a Tensor of shape [b1, …, bB, f1, …, fF] and returns a Tensor whose shape is broadcastable with [b1, …, bB]. Default value: None implies constant zero function.
jitter – float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: “StudentTProcess”.

Raises:

ValueError – if mean_fn is not None and is not callable.

inferpy.models.ConditionalTransformedDistribution(*args, **kwargs)¶

Create a random variable for ConditionalTransformedDistribution.

See ConditionalTransformedDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Transformed Distribution.

Parameters:

distribution – The base distribution instance to transform. Typically an instance of Distribution.
bijector – The object responsible for calculating the transformation. Typically an instance of Bijector.
batch_shape – integer vector Tensor which overrides distribution batch_shape; valid only if distribution.is_scalar_batch().
event_shape – integer vector Tensor which overrides distribution event_shape; valid only if distribution.is_scalar_event().
kwargs_split_fn –
Python callable which takes a kwargs dict and returns a tuple of kwargs dict`s for each of the `distribution and bijector parameters respectively. Default value: _default_kwargs_split_fn (i.e.,

`lambda kwargs: (kwargs.get(‘distribution_kwargs’, {}),
kwargs.get(‘bijector_kwargs’, {}))`)
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
parameters – Locals dict captured by subclass constructor, to be used for copy/slice re-instantiation operations.
name – Python str name prefixed to Ops created by this class. Default: bijector.name + distribution.name.

inferpy.models.TransformedDistribution(*args, **kwargs)¶

Create a random variable for TransformedDistribution.

See TransformedDistribution for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct a Transformed Distribution.

Parameters:

distribution – The base distribution instance to transform. Typically an instance of Distribution.
bijector – The object responsible for calculating the transformation. Typically an instance of Bijector.
batch_shape – integer vector Tensor which overrides distribution batch_shape; valid only if distribution.is_scalar_batch().
event_shape – integer vector Tensor which overrides distribution event_shape; valid only if distribution.is_scalar_event().
kwargs_split_fn –
Python callable which takes a kwargs dict and returns a tuple of kwargs dict`s for each of the `distribution and bijector parameters respectively. Default value: _default_kwargs_split_fn (i.e.,

`lambda kwargs: (kwargs.get(‘distribution_kwargs’, {}),
kwargs.get(‘bijector_kwargs’, {}))`)
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
parameters – Locals dict captured by subclass constructor, to be used for copy/slice re-instantiation operations.
name – Python str name prefixed to Ops created by this class. Default: bijector.name + distribution.name.

inferpy.models.Triangular(*args, **kwargs)¶

Create a random variable for Triangular.

See Triangular for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Triangular distributions.

Parameters:

low – Floating point tensor, lower boundary of the output interval. Must have low < high. Default value: 0.
high – Floating point tensor, upper boundary of the output interval. Must have low < high. Default value: 1.
peak – Floating point tensor, mode of the output interval. Must have low <= peak and peak <= high. Default value: 0.5.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: True.
name – Python str name prefixed to Ops created by this class. Default value: ‘Triangular’.

Raises:

InvalidArgumentError – if validate_args=True and one of the following is True: * low >= high. * peak > high. * low > peak.

inferpy.models.TruncatedNormal(*args, **kwargs)¶

Create a random variable for TruncatedNormal.

See TruncatedNormal for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct TruncatedNormal.

All parameters of the distribution will be broadcast to the same shape, so the resulting distribution will have a batch_shape of the broadcast shape of all parameters.

Parameters:

loc – Floating point tensor; the mean of the normal distribution(s) ( note that the mean of the resulting distribution will be different since it is modified by the bounds).
scale – Floating point tensor; the std deviation of the normal distribution(s).
low – float Tensor representing lower bound of the distribution’s support. Must be such that low < high.
high – float Tensor representing upper bound of the distribution’s support. Must be such that low < high.
validate_args – Python bool, default False. When True distribution parameters are checked at run-time.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

inferpy.models.Uniform(*args, **kwargs)¶

Create a random variable for Uniform.

See Uniform for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Uniform distributions.

Parameters:

low – Floating point tensor, lower boundary of the output interval. Must have low < high.
high – Floating point tensor, upper boundary of the output interval. Must have low < high.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

InvalidArgumentError – if low >= high and validate_args=False.

inferpy.models.VariationalGaussianProcess(*args, **kwargs)¶

Create a random variable for VariationalGaussianProcess.

See VariationalGaussianProcess for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Instantiate a VariationalGaussianProcess Distribution.

Parameters:

kernel – PositiveSemidefiniteKernel-like instance representing the GP’s covariance function.
index_points – float Tensor representing finite (batch of) vector(s) of points in the index set over which the VGP is defined. Shape has the form [b1, …, bB, e1, f1, …, fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e1 is the number (size) of index points in each batch (we denote it e1 to distinguish it from the numer of inducing index points, denoted e2 below). Ultimately the VariationalGaussianProcess distribution corresponds to an e1-dimensional multivariate normal. The batch shape must be broadcastable with kernel.batch_shape, the batch shape of inducing_index_points, and any batch dims yielded by mean_fn.
inducing_index_points – float Tensor of locations of inducing points in the index set. Shape has the form [b1, …, bB, e2, f1, …, fF], just like index_points. The batch shape components needn’t be identical to those of index_points, but must be broadcast compatible with them.
variational_inducing_observations_loc – float Tensor; the mean of the (full-rank Gaussian) variational posterior over function values at the inducing points, conditional on observed data. Shape has the form [b1, …, bB, e2], where b1, …, bB is broadcast compatible with other parameters’ batch shapes, and e2 is the number of inducing points.
variational_inducing_observations_scale – float Tensor; the scale matrix of the (full-rank Gaussian) variational posterior over function values at the inducing points, conditional on observed data. Shape has the form [b1, …, bB, e2, e2], where b1, …, bB is broadcast compatible with other parameters and e2 is the number of inducing points.
mean_fn – Python callable that acts on index points to produce a (batch of) vector(s) of mean values at those index points. Takes a Tensor of shape [b1, …, bB, f1, …, fF] and returns a Tensor whose shape is (broadcastable with) [b1, …, bB]. Default value: None implies constant zero function.
observation_noise_variance – float Tensor representing the variance of the noise in the Normal likelihood distribution of the model. May be batched, in which case the batch shape must be broadcastable with the shapes of all other batched parameters (kernel.batch_shape, index_points, etc.). Default value: 0.
predictive_noise_variance – float Tensor representing additional variance in the posterior predictive model. If None, we simply re-use observation_noise_variance for the posterior predictive noise. If set explicitly, however, we use the given value. This allows us, for example, to omit predictive noise variance (by setting this to zero) to obtain noiseless posterior predictions of function values, conditioned on noisy observations.
jitter – float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: “VariationalGaussianProcess”.

Raises:

ValueError – if mean_fn is not None and is not callable.

inferpy.models.VectorDiffeomixture(*args, **kwargs)¶

Create a random variable for VectorDiffeomixture.

See VectorDiffeomixture for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Constructs the VectorDiffeomixture on R^d.

The vector diffeomixture (VDM) approximates the compound distribution

`none p(x) = int p(x | z) p(z) dz, where z is in the K-simplex, and p(x | z) := p(x | loc=sum_k z[k] loc[k], scale=sum_k z[k] scale[k]) `

Parameters:

mix_loc – float-like Tensor with shape [b1, …, bB, K-1]. In terms of samples, larger mix_loc[…, k] ==> Z is more likely to put more weight on its kth component.
temperature – float-like Tensor. Broadcastable with mix_loc. In terms of samples, smaller temperature means one component is more likely to dominate. I.e., smaller temperature makes the VDM look more like a standard mixture of K components.
distribution – tfp.distributions.Distribution-like instance. Distribution from which d iid samples are used as input to the selected affine transformation. Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a VectorDiffeomixture sample and the distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
loc – Length-K list of float-type Tensor`s. The `k-th element represents the shift used for the k-th affine transformation. If the k-th item is None, loc is implicitly 0. When specified, must have shape [B1, …, Bb, d] where b >= 0 and d is the event size.
scale – Length-K list of LinearOperator`s. Each should be positive-definite and operate on a `d-dimensional vector space. The k-th element represents the scale used for the k-th affine transformation. LinearOperator`s must have shape `[B1, …, Bb, d, d], b >= 0, i.e., characterizes b-batches of d x d matrices
quadrature_size – Python int scalar representing number of quadrature points. Larger quadrature_size means q_N(x) better approximates p(x).
quadrature_fn – Python callable taking normal_loc, normal_scale, quadrature_size, validate_args and returning tuple(grid, probs) representing the SoftmaxNormal grid and corresponding normalized weight. normalized) weight. Default value: quadrature_scheme_softmaxnormal_quantiles.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if not scale or len(scale) < 2.
ValueError – if len(loc) != len(scale)
ValueError – if quadrature_grid_and_probs is not None and len(quadrature_grid_and_probs[0]) != len(quadrature_grid_and_probs[1])
ValueError – if validate_args and any not scale.is_positive_definite.
TypeError – if any scale.dtype != scale[0].dtype.
TypeError – if any loc.dtype != scale[0].dtype.
NotImplementedError – if len(scale) != 2.
ValueError – if not distribution.is_scalar_batch.
ValueError – if not distribution.is_scalar_event.

inferpy.models.VectorExponentialDiag(*args, **kwargs)¶

Create a random variable for VectorExponentialDiag.

See VectorExponentialDiag for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Vector Exponential distribution supported on a subset of R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T.

`none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) `

where:

scale_diag.shape = [k], and,
scale_identity_multiplier.shape = [].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.VectorLaplaceDiag(*args, **kwargs)¶

Create a random variable for VectorLaplaceDiag.

See VectorLaplaceDiag for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Vector Laplace distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = 2 * scale @ scale.T.

`none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) `

where:

scale_diag.shape = [k], and,
scale_identity_multiplier.shape = [].

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.VectorSinhArcsinhDiag(*args, **kwargs)¶

Create a random variable for VectorSinhArcsinhDiag.

See VectorSinhArcsinhDiag for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct VectorSinhArcsinhDiag distribution on R^k.

The arguments scale_diag and scale_identity_multiplier combine to define the diagonal scale referred to in this class docstring:

`none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) `

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this

Additional leading dimensions (if any) will index batches.

Parameters:

loc – Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, …, Bb, k] where b >= 0 and k is the event size.
scale_diag – Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, …, Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier – Non-zero, floating-point Tensor representing a scale-identity-matrix added to scale. May have shape [B1, …, Bb], b >= 0, and characterizes b-batches of scale k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
skewness – Skewness parameter. floating-point Tensor with shape broadcastable with event_shape.
tailweight – Tailweight parameter. floating-point Tensor with shape broadcastable with event_shape.
distribution – tf.Distribution-like instance. Distribution from which k iid samples are used as input to transformation F. Default is tfd.Normal(loc=0., scale=1.). Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a VectorSinhArcsinhDiag sample and distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if at most scale_identity_multiplier is specified.

inferpy.models.VonMises(*args, **kwargs)¶

Create a random variable for VonMises.

See VonMises for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct von Mises distributions with given location and concentration.

The parameters loc and concentration must be shaped in a way that supports broadcasting (e.g. loc + concentration is a valid operation).

Parameters:

loc – Floating point tensor, the circular means of the distribution(s).
concentration – Floating point tensor, the level of concentration of the distribution(s) around loc. Must take non-negative values. concentration = 0 defines a Uniform distribution, while concentration = +inf indicates a Deterministic distribution at loc.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

TypeError – if loc and concentration are different dtypes.

inferpy.models.VonMisesFisher(*args, **kwargs)¶

Create a random variable for VonMisesFisher.

See VonMisesFisher for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Creates a new VonMisesFisher instance.

Parameters:

mean_direction – Floating-point Tensor with shape [B1, … Bn, D]. A unit vector indicating the mode of the distribution, or the unit-normalized direction of the mean. (This is not in general the mean of the distribution; the mean is not generally in the support of the distribution.) NOTE: D is currently restricted to <= 5.
concentration – Floating-point Tensor having batch shape [B1, … Bn] broadcastable with mean_direction. The level of concentration of samples around the mean_direction. concentration=0 indicates a uniform distribution over the unit hypersphere, and concentration=+inf indicates a Deterministic distribution (delta function) at mean_direction.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – For known-bad arguments, i.e. unsupported event dimension.

inferpy.models.Wishart(*args, **kwargs)¶

Create a random variable for Wishart.

See Wishart for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Construct Wishart distributions.

Parameters:

df – float or double Tensor. Degrees of freedom, must be greater than or equal to dimension of the scale matrix.
scale – float or double Tensor. The symmetric positive definite scale matrix of the distribution. Exactly one of scale and ‘scale_tril` must be passed.
scale_tril – float or double Tensor. The Cholesky factorization of the symmetric positive definite scale matrix of the distribution. Exactly one of scale and ‘scale_tril` must be passed.
input_output_cholesky – Python bool. If True, functions whose input or output have the semantics of samples assume inputs are in Cholesky form and return outputs in Cholesky form. In particular, if this flag is True, input to log_prob is presumed of Cholesky form and output from sample, mean, and mode are of Cholesky form. Setting this argument to True is purely a computational optimization and does not change the underlying distribution; for instance, mean returns the Cholesky of the mean, not the mean of Cholesky factors. The variance and stddev methods are unaffected by this flag. Default value: False (i.e., input/output does not have Cholesky semantics).
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined.
name – Python str name prefixed to Ops created by this class.

Raises:

ValueError – if zero or both of ‘scale’ and ‘scale_tril’ are passed in.

inferpy.models.Zipf(*args, **kwargs)¶

Create a random variable for Zipf.

See Zipf for more details.

Returns:: RandomVariable.

#### Original Docstring for Distribution

Initialize a batch of Zipf distributions.

Parameters:

power – Float like Tensor representing the power parameter. Must be strictly greater than 1.
dtype – The dtype of Tensor returned by sample. Default value: tf.int32.
interpolate_nondiscrete – Python bool. When False, log_prob returns -inf (and prob returns 0) for non-integer inputs. When True, log_prob evaluates the continuous function -power log(k) - log(zeta(power)) , which matches the Zipf pmf at integer arguments k (note that this function is not itself a normalized probability log-density). Default value: True.
sample_maximum_iterations – Maximum number of iterations of allowable iterations in sample. When validate_args=True, samples which fail to reach convergence (subject to this cap) are masked out with self.dtype.min or nan depending on self.dtype.is_integer. Default value: 100.
validate_args – Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
allow_nan_stats – Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value “NaN” to indicate the result is undefined. When False, an exception is raised if one or more of the statistic’s batch members are undefined. Default value: False.
name – Python str name prefixed to Ops created by this class. Default value: ‘Zipf’.

Raises:

TypeError – if power is not float like.

inferpy.models.MixtureGaussian(locs, scales, logits=None, probs=None, *args, **kwargs)[source]¶