tfp.edward2.Wishart

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Create a random variable for Wishart.

Aliases:

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

See Wishart for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

Construct Wishart distributions.

Args:

  • df: float or double Tensor. Degrees of freedom, must be greater than or equal to dimension of the scale matrix.
  • scale: float or double Tensor. The symmetric positive definite scale matrix of the distribution. Exactly one of scale and 'scale_tril` must be passed.
  • scale_tril: float or double Tensor. The Cholesky factorization of the symmetric positive definite scale matrix of the distribution. Exactly one of scale and 'scale_tril` must be passed.
  • input_output_cholesky: Python bool. If True, functions whose input or output have the semantics of samples assume inputs are in Cholesky form and return outputs in Cholesky form. In particular, if this flag is True, input to log_prob is presumed of Cholesky form and output from sample, mean, and mode are of Cholesky form. Setting this argument to True is purely a computational optimization and does not change the underlying distribution; for instance, mean returns the Cholesky of the mean, not the mean of Cholesky factors. The variance and stddev methods are unaffected by this flag. Default value: False (i.e., input/output does not have Cholesky semantics).
  • 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 zero or both of 'scale' and 'scale_tril' are passed in.