tfp.edward2.Mixture

Create a random variable for Mixture.

Aliases:

tfp.experimental.edward2.Mixture

tfp.edward2.Mixture(
    *args,
    **kwargs
)

See Mixture for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

Initialize a Mixture distribution.

A Mixture is defined by a Categorical (cat, representing the mixture probabilities) and a list of Distribution objects all having matching dtype, batch shape, event shape, and continuity properties (the components).

The num_classes of cat must be possible to infer at graph construction time and match len(components).

Args:

cat: A Categorical distribution instance, representing the probabilities of distributions.
components: A list or tuple of Distribution instances. Each instance must have the same type, be defined on the same domain, and have matching event_shape and batch_shape.
validate_args: Python bool, default False. If True, raise a runtime error if batch or event ranks are inconsistent between cat and any of the distributions. This is only checked if the ranks cannot be determined statically at graph construction time.
allow_nan_stats: Boolean, 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.
use_static_graph: Calls to sample will not rely on dynamic tensor indexing, allowing for some static graph compilation optimizations, but at the expense of sampling all underlying distributions in the mixture. (Possibly useful when running on TPUs). Default value: False (i.e., use dynamic indexing).
name: A name for this distribution (optional).

Raises:

TypeError: If cat is not a Categorical, or components is not a list or tuple, or the elements of components are not instances of Distribution, or do not have matching dtype.
ValueError: If components is an empty list or tuple, or its elements do not have a statically known event rank. If cat.num_classes cannot be inferred at graph creation time, or the constant value of cat.num_classes is not equal to len(components), or all components and cat do not have matching static batch shapes, or all components do not have matching static event shapes.