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Feedback Message Passing for Inference in Gaussian Graphical Models

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dc.contributor.author Liu, Ying
dc.contributor.author Chandrasekaran, Venkat
dc.contributor.author Anandkumar, Animashree
dc.contributor.author Willsky, Alan S.
dc.date.accessioned 2012-10-03T19:23:49Z
dc.date.available 2012-10-03T19:23:49Z
dc.date.issued 2010-07
dc.date.submitted 2010-06
dc.identifier.isbn 978-1-4244-7891-0
dc.identifier.isbn 978-1-4244-7890-3
dc.identifier.uri http://hdl.handle.net/1721.1/73579
dc.description.abstract For Gaussian graphical models with cycles, loopy belief propagation often performs reasonably well, but its convergence is not guaranteed and the computation of variances is generally incorrect. In this paper, we identify a set of special vertices called a feedback vertex set whose removal results in a cycle-free graph. We propose a feedback message passing algorithm in which non-feedback nodes send out one set of messages while the feedback nodes use a different message update scheme. Exact inference results can be obtained in O(k[subscript 2]n), where k is the number of feedback nodes and n is the total number of nodes. For graphs with large feedback vertex sets, we describe a tractable approximate feedback message passing algorithm. Experimental results show that this procedure converges more often, faster, and provides better results than loopy belief propagation. en_US
dc.language.iso en_US
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en_US
dc.relation.isversionof http://dx.doi.org/10.1109/ISIT.2010.5513321 en_US
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. en_US
dc.source IEEE en_US
dc.title Feedback Message Passing for Inference in Gaussian Graphical Models en_US
dc.type Article en_US
dc.identifier.citation Liu, Ying et al. “Feedback Message Passing for Inference in Gaussian Graphical Models.” IEEE International Symposium on Information Theory Proceedings (ISIT), 2010. 1683–1687. © Copyright 2010 IEEE en_US
dc.contributor.department Massachusetts Institute of Technology. Laboratory for Information and Decision Systems en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science en_US
dc.contributor.mitauthor Liu, Ying
dc.contributor.mitauthor Chandrasekaran, Venkat
dc.contributor.mitauthor Anandkumar, Animashree
dc.contributor.mitauthor Willsky, Alan S.
dc.relation.journal Proceedings of the IEEE International Symposium on Information Theory Proceedings (ISIT), 2010 en_US
dc.identifier.mitlicense PUBLISHER_POLICY en_US
dc.eprint.version Final published version en_US
dc.type.uri http://purl.org/eprint/type/ConferencePaper en_US
dspace.orderedauthors Liu, Ying; Chandrasekaran, Venkat; Anandkumar, Animashree; Willsky, Alan S. en


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