Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference


Matthias Seeger, Hannes Nickisch ;
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:652-660, 2011.


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. [pdf]

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