Create a random variable for Mixture.
cat, components, validate_args=False, allow_nan_stats=True,
See Mixture for more details.
Original Docstring for Distribution
Initialize a Mixture distribution.
Mixture is defined by a
cat, representing the
mixture probabilities) and a list of
all having matching dtype, batch shape, event shape, support, and continuity
properties (the components).
cat must be possible to infer at graph construction
time and match
Categorical distribution instance, representing the probabilities
A list or tuple of
Each instance must have the same type, be defined on the same domain,
and have matching
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.
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.
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).
False (i.e., use dynamic indexing).
A name for this distribution (optional).
If cat is not a
components is not
a list or tuple, or the elements of
components are not
Distribution, or do not have matching
components is an empty list or tuple, or its
elements do not have a statically known event rank.
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
cat do not have
matching static batch shapes, or all components do not
have matching static event shapes.