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	Convergence Rate Analysis of Gaussian Belief Propagation for Markov Networks
	Zhaorong Zhang; Minyue Fu
刊名	IEEE/CAA Journal of Automatica Sinica
	2020
卷号	7期号:3页码:668-673
关键词	Belief propagation distributed algorithm distributed estimation Gaussian belief propagation Markov networks
ISSN号	2329-9266
DOI	10.1109/JAS.2020.1003105
英文摘要	Gaussian belief propagation algorithm (GaBP) is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network in a finite number of iterations when the underlying Gaussian graph is acyclic. It is also known more recently that the algorithm produces correct marginal means asymptotically for cyclic Gaussian graphs under the condition of walk summability (or generalised diagonal dominance). This paper extends this convergence result further by showing that the convergence is exponential under the generalised diagonal dominance condition, and provides a simple bound for the convergence rate. Our results are derived by combining the known walk summability approach for asymptotic convergence analysis with the control systems approach for stability analysis.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42977]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Zhaorong Zhang,Minyue Fu. Convergence Rate Analysis of Gaussian Belief Propagation for Markov Networks[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(3):668-673.
APA	Zhaorong Zhang,&Minyue Fu.(2020).Convergence Rate Analysis of Gaussian Belief Propagation for Markov Networks.IEEE/CAA Journal of Automatica Sinica,7(3),668-673.
MLA	Zhaorong Zhang,et al."Convergence Rate Analysis of Gaussian Belief Propagation for Markov Networks".IEEE/CAA Journal of Automatica Sinica 7.3(2020):668-673.

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