tfp.edward2.Binomial

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Create a random variable for Binomial.

Aliases:

• tfp.experimental.edward2.Binomial

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

See Binomial for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

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.