This function handles conditioning of multi-output GPs in the case where the conditioning
points are all fully correlated, in both the prior and posterior.
Note: This conditional can handle ‘repetitions’ R, given in f and q_sqrt.
Parameters
Kmn (Tensor) – [M, N, P]
Kmm (Tensor) – [M, M]
Knn (Tensor) – [N, P] or [N, P, P] or [P, N, N] or [N, P, N, P]
f (Tensor) – data matrix, [M, R]
q_sqrt (Optional[Tensor]) – [R, M, M] or [M, R]
full_cov (bool) – calculate covariance between inputs
full_output_cov (bool) – calculate covariance between outputs
white (bool) – use whitened representation
Return type
Tuple[Tensor, Tensor]
Returns
mean: [R, N, P]
variance: [R, N, P], [R, N, P, P], [R, P, N, N], [R, N, P, N, P]