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Improve sampling from GP predictive posteriors. #2498

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Improve sampling from GP predictive posteriors. #2498

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4 changes: 2 additions & 2 deletions gpytorch/likelihoods/gaussian_likelihood.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
from typing import Any, Optional, Tuple, Union

import torch
from linear_operator.operators import LinearOperator, MaskedLinearOperator, ZeroLinearOperator
from linear_operator.operators import LinearOperator, MaskedLinearOperator, PsdSumLinearOperator, ZeroLinearOperator
from torch import Tensor
from torch.distributions import Distribution, Normal

Expand Down Expand Up @@ -114,7 +114,7 @@ def log_marginal(
def marginal(self, function_dist: MultivariateNormal, *params: Any, **kwargs: Any) -> MultivariateNormal:
mean, covar = function_dist.mean, function_dist.lazy_covariance_matrix
noise_covar = self._shaped_noise_covar(mean.shape, *params, **kwargs)
full_covar = covar + noise_covar
full_covar = PsdSumLinearOperator(covar, noise_covar)
return function_dist.__class__(mean, full_covar)


Expand Down