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  Assessing Approximate Inference for Binary Gaussian Process Classification

Kuss, M., & Rasmussen, C. (2005). Assessing Approximate Inference for Binary Gaussian Process Classification. The Journal of Machine Learning Research, 6, 1679-1704.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D3E9-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-D774-7
Genre: Journal Article

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 Creators:
Kuss, M1, 2, Author              
Rasmussen, C1, 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 priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace‘s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace‘s method.

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 Dates: 2005-10
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3593
 Degree: -

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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 6 Sequence Number: - Start / End Page: 1679 - 1704 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1