gpflow.conditionals.sample_conditionals#

Functions#

gpflow.conditionals.sample_conditionals.sample_mvn#

gpflow.conditionals.sample_conditionals.sample_mvn(mean, cov, full_cov, num_samples=None)[source]#

Returns a sample from a D-dimensional Multivariate Normal distribution.

Parameters

  • mean (Tensor) – […, N, D]

  • cov (Tensor) – […, N, D] or […, N, D, D]

  • full_cov (bool) – if True return a “full” covariance matrix, otherwise a “diag”: - “full”: cov holds the full covariance matrix (without jitter) - “diag”: cov holds the diagonal elements of the covariance matrix

  • num_samples (Optional[int]) –

Return type

Tensor

Returns

sample from the MVN of shape […, (S), N, D], S = num_samples