tfp.edward2.MultivariateNormalTriL

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

Create a random variable for MultivariateNormalTriL.

See MultivariateNormalTriL for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

Construct Multivariate Normal distribution on R^k.

The batch_shape is the broadcast shape between loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this.

Recall that covariance = scale @ scale.T. A (non-batch) scale matrix is:

scale = scale_tril

where scale_tril is lower-triangular k x k matrix with non-zero diagonal, i.e., tf.diag_part(scale_tril) != 0.

Additional leading dimensions (if any) will index batches.

Args:

  • loc: Floating-point Tensor. If this is set to None, loc is implicitly 0. When specified, may have shape [B1, ..., Bb, k] where b >= 0 and k is the event size.
  • scale_tril: Floating-point, lower-triangular Tensor with non-zero diagonal elements. scale_tril has shape [B1, ..., Bb, k, k] where b >= 0 and k is the event size.
  • 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 neither loc nor scale_tril are specified.