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  Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Seeger, M., & Nickisch, H.(2010). Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BD46-F Version Permalink: https://hdl.handle.net/21.11116/0000-0002-8580-6
Genre: Report

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https://arxiv.org/abs/1012.3584 (Any fulltext)
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 Creators:
Seeger, M, Author           
Nickisch, H1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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 (Opperamp;Winther 05) with covariance decoupling techniques (Wipfamp;Nagarajan 08, Nickischamp;Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.

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 Dates: 2010-12
 Publication Status: Issued
 Pages: 16
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 6995
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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