Create a random variable for Empirical.
samples, event_ndims=0, validate_args=False, allow_nan_stats=True,
See Empirical for more details.
Original Docstring for Distribution
Tensor of shape [B1, ..., Bk, S, E1, ..., En]
,k, n >= 0
. Samples or batches of samples on which the distribution
is based. The firstk
dimensions index into a batch of independent
distributions. Length ofS
dimension determines number of samples
in each multiset. The lastn
dimension represents samples for each
distribution. n is specified by argument event_ndims.
. number of dimensions for each
this distribution has scalar samples. When1
distribution has vector-like samples.
parameters are checked for validity despite possibly degrading runtime
invalid inputs may silently render incorrect
(e.g., mean, mode, variance) use the valueNaN
to indicate the
result is undefined. WhenFalse
, an exception is raised if one or
more of the statistic's batch members are undefined.
Pythonstr` name prefixed to Ops created by this class.
if the rank of
samples is statically known and less than
event_ndims + 1.