Feedback Message Passing for Inference in Gaussian Graphical Models
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Willsky_Feedback message.pdf
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Author(s)
Liu, Ying
Chandrasekaran, Venkat
Anandkumar, Animashree
Willsky, Alan S.
Date Issued
July 2010
Journal
Proceedings of the IEEE International Symposium on Information Theory Proceedings (ISIT), 2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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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.
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DOI of Published Version
https://doi.org/10.1109/ISIT.2010.5513321
