Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
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.
Cite
Text
Seeger and Nickisch. "Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Seeger and Nickisch. "Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/seeger2011aistats-fast/)BibTeX
@inproceedings{seeger2011aistats-fast,
title = {{Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference}},
author = {Seeger, Matthias and Nickisch, Hannes},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {652-660},
volume = {15},
url = {https://mlanthology.org/aistats/2011/seeger2011aistats-fast/}
}