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Approximations for binary Gaussian process classification. (English) Zbl 1225.62087
Summary: We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between several approaches are elucidated theoretically, and the properties of the different algorithms are corroborated by experimental results. We examine both 1) the quality of the predictive distributions and 2) the suitability of the different marginal likelihood approximations for model selection (selecting hyperparameters) and compare them to a gold standard based on MCMC. Interestingly, some methods produce good predictive distributions although their marginal likelihood approximations are poor. Strong conclusions are drawn about the methods: The Expectation Propagation algorithm is almost always the method of choice unless the computational budget is very tight. We also extend existing methods in various ways, and provide unifying code implementing all approaches.

MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
65C40 Numerical analysis or methods applied to Markov chains
68T05 Learning and adaptive systems in artificial intelligence
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