pmrf.distributions.parameter

Classes

JointParameterDistribution(param_groups, ...)

class pmrf.distributions.parameter.JointParameterDistribution(param_groups, param_names, kind='prior')[source]

Bases: Distribution

Parameters:

param_groups (list[ParameterGroup])
param_names (list[str])

reparametrized_params = []

has_rsample = True

sample(key, sample_shape=())[source]

Returns a sample from the distribution having shape given by sample_shape + batch_shape + event_shape. Note that when sample_shape is non-empty, leading dimensions (of size sample_shape) of the returned sample will be filled with iid draws from the distribution instance.

Parameters:

key (jax.random.PRNGKey) – the rng_key key to be used for the distribution.
sample_shape (tuple) – the sample shape for the distribution.

Returns:

an array of shape sample_shape + batch_shape + event_shape

Return type:

numpy.ndarray

log_prob(value)[source]

Evaluates the log probability density for a batch of samples given by value.

Parameters:: value – A batch of samples from the distribution.
Returns:: an array with shape value.shape[:-self.event_shape]
Return type:: numpy.ndarray

icdf(u)[source]

The inverse cumulative distribution function of this distribution.

Parameters:: q – quantile values, should belong to [0, 1].
Returns:: the samples whose cdf values equals to q.

cdf(x)[source]

The cumulative distribution function of this distribution.

Parameters:: value – samples from this distribution.
Returns:: output of the cumulative distribution function evaluated at value.

support()[source]