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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.

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 Creators:
Seeger, M1, Author           
Nickisch, H1, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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: -
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 Rev. Type: -
 Identifiers: URI: http://arxiv.org/abs/1012.3584
BibTex Citekey: 6995
 Degree: -

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