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2005 JMLR JMLR 2005

Assessing Approximate Inference for Binary Gaussian Process Classification

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. [abs] [ pdf ][ bib ] © JMLR 2005. (edit, beta)

📈 Trend Setter — Bayesian Inference
🧭 Keyword Pioneer — predictive performance
🐣 Hot Topic Early Bird — markov chain monte carlo
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors

Malte Kuss , Carl Edward Rasmussen

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Variational Inference Machine Learning > Bayesian & Probabilistic > Gaussian Processes

Keywords

approximate inference bayesian inference markov chain monte carlo laplace approximation gaussian process expectation propagation gaussian process classification predictive performance
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