tfp.edward2.VectorLaplaceDiag

Create a random variable for VectorLaplaceDiag.

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

See VectorLaplaceDiag for more details.

RandomVariable.

Construct Vector Laplace 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 = 2 * scale @ scale.T.

scale = diag(scale_diag + scale_identity_multiplier * ones(k))

where:

Additional leading dimensions (if any) will index batches.

If both scale_diag and scale_identity_multiplier are None, then scale is the Identity matrix.

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_diag: Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, ..., Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
scale_identity_multiplier: Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, ..., Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are None then scale is the Identity.
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.