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tfp.edward2.ProbitBernoulli

View source on GitHub

Create a random variable for ProbitBernoulli.

Aliases:

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

See ProbitBernoulli for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

Construct ProbitBernoulli distributions.

Args:

  • probits: An N-D Tensor representing the probit-odds of a 1 event. Each entry in the Tensor parametrizes an independent ProbitBernoulli distribution where the probability of an event is normal_cdf(probits). Only one of probits or probs should be passed in.
  • probs: An N-D Tensor representing the probability of a 1 event. Each entry in the Tensor parameterizes an independent ProbitBernoulli distribution. Only one of probits or probs should be passed in.
  • dtype: The type of the event samples. Default: int32.
  • 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.

Raises:

  • ValueError: If probs and probits are passed, or if neither are passed.