tfp.edward2.Binomial

Create a random variable for Binomial.

Main aliases

tfp.experimental.edward2.Binomial

@classmethod
tfp.edward2.Binomial(
    total_count, logits=None, probs=None, validate_args=False, allow_nan_stats=True,
    name=None
)

See Binomial for more details.

Returns
RandomVariable.

Initialize a batch of Binomial distributions.

Args
`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.

Returns