Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian 
Inference

Seeger, M; Nickisch, H

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Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

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Nickisch, H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://arxiv.org/abs/1012.3584
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Citation

Seeger, M., & Nickisch, H.(2010). Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BD46-F

Abstract

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opperamp;Winther 05) with covariance decoupling techniques (Wipfamp;Nagarajan 08, Nickischamp;Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.