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Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84205

Seeger,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84109

Nickisch,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Seeger, M., & Nickisch, H. (2011). Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. In JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011 (pp. 652-660). Cambridge, MA, USA: MIT Press.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BC3C-F
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 (OpperWinther, 2005) with covariance decoupling techniques (WipfNagarajan, 2008; NickischSeeger, 2009), it runs at least an order of magnitude faster than the most common EP solver.