Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

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.