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  Approximate Inference for Robust Gaussian Process Regression

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D703-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-8826-A
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MPIK-TR-136.pdf (Publisher version), 464KB
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 Creators:
Kuss, M1, 2, Author           
Pfingsten, T1, 2, Author           
Csato, L1, 2, Author           
Rasmussen, CE1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2005-03
 Publication Status: Issued
 Pages: 27
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 136
BibTex Citekey: 3265
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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Pages: - Volume / Issue: 136 Sequence Number: - Start / End Page: - Identifier: -