Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
Machine Learning
2010-12-17 v1
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 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.
Cite
@article{arxiv.1012.3584,
title = {Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference},
author = {Matthias W. Seeger and Hannes Nickisch},
journal= {arXiv preprint arXiv:1012.3584},
year = {2010}
}
Comments
16 pages, 3 figures, submitted for conference publication