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  Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Seeger, M., & Nickisch, H. (2011). Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. In G. Gordon, D. Dunson, & M. Dudik (Eds.), JMLR Workshop and Conference Proceedings (pp. 652-660). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BC3C-F Version Permalink: http://hdl.handle.net/21.11116/0000-0002-0D04-C
Genre: Conference Paper

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 Creators:
Seeger, M, Author              
Nickisch, H1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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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 (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.

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 Dates: 2011-04
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: SeegerN2011
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Title: 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011)
Place of Event: Fort Lauderdale, FL, USA
Start-/End Date: 2011-04-11 - 2011-04-13

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Proceedings
 Creator(s):
Gordon, G, Editor
Dunson, D, Editor
Dudik, M, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: 15 Sequence Number: - Start / End Page: 652 - 660 Identifier: -