Max – Planck – Institut f ür biologische Kybernetik Max Planck Institute for Biological Cybernetics Technical Report No . 136 Approximate Inference for Robust Gaussian Process Regression
Gaussian process (GP) priors have been successfully used in non-parametric Bayesian regression and classification models. Inference can be performed analytically only for the regression model with Gaussian noise. For all other likelihood models inference is intractable and various approximation techniques have been proposed. In recent years xpectation-propagation(EP) has been developed as a general method for approximate inference. This article provides a general summary of how expectationpropagation can be used for approximate inference in Gaussian process models. Furthermore we present a case study describing its implementation for a new robust variant of Gaussian process regression. To gain further insights into the quality of the EP approximation we present experiments in which we compare to results obtained by Markov chain Monte Carlo(MCMC) sampling.