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Conference Paper

Assessing Approximations for Gaussian Process Classification

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84030

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

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

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Citation

Kuss, M., & Rasmussen, C. (2006). Assessing Approximations for Gaussian Process Classification. Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, 699-706.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D1DF-C
Abstract
Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplaceamp;amp;amp;amp;amp;amp;amp;amp;lsquo;s method and Expectation Propagation (EP) focusing on marginal likelihood estimates and predictive performance. We explain theoretically and corroborate empirically that EP is superior to Laplace. We also compare to a sophisticated MCMC scheme and show that EP is surprisingly accurate.