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Assessing Approximations for Gaussian Process Classification
Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplace‘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.
@inproceedings{3530, title = {Assessing Approximations for Gaussian Process Classification}, journal = {Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference}, booktitle = {Advances in neural information processing systems 18}, abstract = {Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplace‘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.}, pages = {699-706}, editors = {Weiss, Y. , B. Sch{\"o}lkopf, J. Platt}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = may, year = {2006}, slug = {3530}, author = {Kuss, M. and Rasmussen, CE.}, month_numeric = {5} }