Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
Matthias Seeger, Hannes Nickisch · arXiv (Cornell University) · 2010
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 05) with covariance decoupling techniques (Wipf&Nagarajan 08, Nickisch&Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.