Missed TensorFlow World? Check out the recap. Learn more

tfp.edward2.LKJ

View source on GitHub

Create a random variable for LKJ.

Aliases:

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

See LKJ for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

Construct LKJ distributions.

Args:

  • dimension: Python int. The dimension of the correlation matrices to sample.
  • concentration: float or double Tensor. The positive concentration parameter of the LKJ distributions. The pdf of a sample matrix X is proportional to det(X) ** (concentration - 1).
  • 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 is of Cholesky form. Setting this argument to True is purely a computational optimization and does not change the underlying distribution. Additionally, validation checks which are only defined on the multiplied-out form are omitted, even if validate_args is True. 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 dimension is negative.