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- Publisher Website: 10.1109/TSP.2015.2465303
- Scopus: eid_2-s2.0-84960153747
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Article: Distributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement
Title | Distributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement |
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Authors | |
Keywords | Accuracy improvement belief propagation Gaussian graphical model variance accuracy analysis |
Issue Date | 2015 |
Publisher | IEEE. 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? |
Abstract | Belief 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 Identifier | http://hdl.handle.net/10722/231937 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.520 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | SU, Q | - |
dc.contributor.author | Wu, YC | - |
dc.date.accessioned | 2016-09-20T05:26:29Z | - |
dc.date.available | 2016-09-20T05:26:29Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | IEEE Transactions on Signal Processing, 2015, v. 63 n. 23, p. 6258-6271 | - |
dc.identifier.issn | 1053-587X | - |
dc.identifier.uri | http://hdl.handle.net/10722/231937 | - |
dc.description.abstract | Belief 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.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78 | - |
dc.relation.ispartof | IEEE Transactions on Signal Processing | - |
dc.rights | IEEE 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.subject | Accuracy improvement | - |
dc.subject | belief propagation | - |
dc.subject | Gaussian graphical model | - |
dc.subject | variance accuracy analysis | - |
dc.title | Distributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement | - |
dc.type | Article | - |
dc.identifier.email | Wu, YC: ycwu@eee.hku.hk | - |
dc.identifier.authority | Wu, YC=rp00195 | - |
dc.identifier.doi | 10.1109/TSP.2015.2465303 | - |
dc.identifier.scopus | eid_2-s2.0-84960153747 | - |
dc.identifier.hkuros | 264928 | - |
dc.identifier.volume | 63 | - |
dc.identifier.issue | 23 | - |
dc.identifier.spage | 6258 | - |
dc.identifier.epage | 6271 | - |
dc.identifier.isi | WOS:000364855000007 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1053-587X | - |