Source code for botorch.posteriors.fully_bayesian

# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from botorch.posteriors.gpytorch import GPyTorchPosterior
from gpytorch.distributions.multivariate_normal import MultivariateNormal
from torch import Tensor

[docs]class FullyBayesianPosterior(GPyTorchPosterior): r"""A posterior for a fully Bayesian model.""" def __init__( self, mvn: MultivariateNormal, marginalize_over_mcmc_samples: bool = False ) -> None: r"""A posterior for a fully Bayesian model. The MCMC batch dimension is -3. Args: mvn: A GPyTorch MultivariateNormal (single-output case) marginalize_over_mcmc_samples: If true, use the law of total variance to marginalize over the hyperparameter samples. This should always be false when computing acquisition functions. """ super().__init__(mvn=mvn) self._mean = mvn.mean.unsqueeze(-1) self._variance = mvn.covariance_matrix.diagonal(dim1=-2, dim2=-1).unsqueeze(-1) if marginalize_over_mcmc_samples: num_mcmc_samples = self._variance.shape[-3] t1 = self._variance.sum(dim=-3) / num_mcmc_samples t2 = self._mean.pow(2).sum(dim=-3) / num_mcmc_samples t3 = -(self._mean.sum(dim=-3) / num_mcmc_samples).pow(2) self._variance = t1 + t2 + t3 self._mean = self._mean.mean(dim=-3) self.mvn = None @property def mean(self) -> Tensor: r"""The posterior mean.""" return self._mean @property def variance(self) -> Tensor: r"""The posterior variance.""" return self._variance