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Approximate Inference for Robust Gaussian Process Regression

MPS-Authors
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Kuss,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Pfingsten,  T
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Csato,  L
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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MPIK-TR-136.pdf
(Publisher version), 464KB

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Citation

Kuss, M., Pfingsten, T., Csato, L., & Rasmussen, C.(2005). Approximate Inference for Robust Gaussian Process Regression (136). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D703-4
Abstract
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
expectation-propagation (EP) has been developed as a general method for approximate inference. This article provides a general summary of how expectation-propagation 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.