Source code for gpytorch.priors.prior

#!/usr/bin/env python3

from abc import ABC

from torch.nn import Module

from ..distributions import Distribution

[docs]class Prior(Distribution, Module, ABC): """ Base class for Priors in GPyTorch. In GPyTorch, a parameter can be assigned a prior by passing it as the `prior` argument to :func:`~gpytorch.module.register_parameter`. GPyTorch performs internal bookkeeping of priors, and for each parameter with a registered prior includes the log probability of the parameter under its respective prior in computing the Marginal Log-Likelihood. """ def transform(self, x): return self._transform(x) if self._transform is not None else x
[docs] def log_prob(self, x): r""" :return: log-probability of the parameter value under the prior :rtype: torch.Tensor """ return super(Prior, self).log_prob(self.transform(x))