Journal article

A tutorial on variational Bayesian inference

Abstract:: This tutorial describes the mean-field variational Bayesian approximation to inference in graphical models, using modern machine learning terminology rather than statistical physics concepts. It begins by seeking to find an approximate mean-field distribution close to the target joint in the KL-divergence sense. It then derives local node updates and reviews the recent Variational Message Passing framework. © Springer Science+Business Media B.V. 2011.

Publication status:: Published

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APA Style

Fox, C., & Roberts, S. (2012). A tutorial on variational Bayesian inference. ARTIFICIAL INTELLIGENCE REVIEW, 38(2), 85–95.

MLA Style

Fox, C., and S. Roberts. “A Tutorial on Variational Bayesian Inference.” ARTIFICIAL INTELLIGENCE REVIEW, vol. 38, no. 2, 2012, pp. 85–95.

Chicago Style

Fox, C, and S Roberts. 2012. “A Tutorial on Variational Bayesian Inference.” ARTIFICIAL INTELLIGENCE REVIEW 38 (2): 85–95.
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Publisher copy:: 10.1007/s10462-011-9236-8

Authors

+ Fox, C More by this author

Role:: Author

+ Roberts, S More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author

Journal:: ARTIFICIAL INTELLIGENCE REVIEW More from this journal
Volume:: 38
Issue:: 2
Pages:: 85-95
Publication date:: 2012-08-01
DOI:: 10.1007/s10462-011-9236-8
EISSN:: 1573-7462
ISSN:: 0269-2821

Language:: English
Keywords:: Tutorial

Mean-field

Variational Bayes
Pubs id:: pubs:350322
UUID:: uuid:0a64e4af-a9fd-40a8-8d00-eef52b3a9213
Local pid:: pubs:350322
Source identifiers:: 350322
Deposit date:: 2013-11-17

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Copyright date:: 2012

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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