Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Matthias Seeger, Hannes Nickisch; JMLR W&CP 15:652-660, 2011.

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 (Opper&Winther, 2005) with covariance decoupling techniques (Wipf&Nagarajan, 2008; Nickisch&Seeger, 2009), it runs at least an order of magnitude faster than the most common EP solver.

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