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Article: Distributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement

TitleDistributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement
Authors
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78
Citation
IEEE Transactions on Signal Processing, 2015, v. 63 n. 23, p. 6258-6271 How to Cite?
AbstractBelief propagation (BP) is an efficient algorithm for calculating approximate marginal probability density function (PDF) in large-scale Gaussian graphical models. It is known that when BP converges, the mean calculated by BP is the exact mean of the marginal PDF, while the accuracy of the variance calculated by BP is in general poor and unpredictable. In this paper, an explicit error expression of the variance calculated by BP is derived. By novel representation of this error expression, a distributed message-passing algorithm is proposed to improve the accuracy of the variance calculated by BP. It is proved that the upper bound of the residual error in the improved variance monotonically decreases as the number of selected nodes in a particular set increases, and eventually vanishes to zero as the remaining graph becomes loop-free after removal of the selected nodes. Numerical examples are presented to illustrate the effectiveness of the proposed algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/231937
ISSN
2015 Impact Factor: 2.624
2015 SCImago Journal Rankings: 2.004

 

DC FieldValueLanguage
dc.contributor.authorSU, Q-
dc.contributor.authorWu, YC-
dc.date.accessioned2016-09-20T05:26:29Z-
dc.date.available2016-09-20T05:26:29Z-
dc.date.issued2015-
dc.identifier.citationIEEE Transactions on Signal Processing, 2015, v. 63 n. 23, p. 6258-6271-
dc.identifier.issn1053-587X-
dc.identifier.urihttp://hdl.handle.net/10722/231937-
dc.description.abstractBelief propagation (BP) is an efficient algorithm for calculating approximate marginal probability density function (PDF) in large-scale Gaussian graphical models. It is known that when BP converges, the mean calculated by BP is the exact mean of the marginal PDF, while the accuracy of the variance calculated by BP is in general poor and unpredictable. In this paper, an explicit error expression of the variance calculated by BP is derived. By novel representation of this error expression, a distributed message-passing algorithm is proposed to improve the accuracy of the variance calculated by BP. It is proved that the upper bound of the residual error in the improved variance monotonically decreases as the number of selected nodes in a particular set increases, and eventually vanishes to zero as the remaining graph becomes loop-free after removal of the selected nodes. Numerical examples are presented to illustrate the effectiveness of the proposed algorithm.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78-
dc.relation.ispartofIEEE Transactions on Signal Processing-
dc.rightsIEEE Transactions on Signal Processing. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. -
dc.titleDistributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement-
dc.typeArticle-
dc.identifier.emailWu, YC: ycwu@eee.hku.hk-
dc.identifier.authorityWu, YC=rp00195-
dc.identifier.doi10.1109/TSP.2015.2465303-
dc.identifier.hkuros264928-
dc.identifier.volume63-
dc.identifier.issue23-
dc.identifier.spage6258-
dc.identifier.epage6271-
dc.publisher.placeUnited States-

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