Convergence analysis of the information matrix in Gaussian Belief Propagation
Du J.1; Ma S.3; Wu Y.-C.2; Kar S.1; Moura J.M.F.1
Conference NameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Source PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Conference DateMAR 05-09, 2017
Conference PlaceNew Orleans, LA

Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local meansurements/observations are scattered over a wide geographical area. However, the convergence of Gaussian BP is still an open issue. In this paper, we consider the convergence of Gaussian BP, focusing in particular on the convergence of the information matrix. We show analytically that the exchanged message information matrix converges for arbitrary positive semidefinite initial value, and its distance to the unique positive definite limit matrix decreases exponentially fast.

KeywordBelief Propagation Graphical Model Large-scale Networks Markov Random Field
URLView the original
Indexed BySCIE
WOS Research AreaAcoustics ; Engineering
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS IDWOS:000414286204047
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Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Affiliation1.Carnegie Mellon University
2.The University of Hong Kong
3.Universidade de Macau
Recommended Citation
GB/T 7714
Du J.,Ma S.,Wu Y.-C.,et al. Convergence analysis of the information matrix in Gaussian Belief Propagation[C],2017:4074-4078.
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