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Gaussian belief with dynamic data and in dynamic network
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2009 (English)In: Europhysics letters, ISSN 0295-5075, E-ISSN 1286-4854, Vol. 87, no 6Article in journal (Refereed) Published
Abstract [en]

In this paper we analyze Belief Propagation over a Gaussian model in a dynamic environment. Recently, this has been proposed as a method to average local measurement values by a distributed protocol (Consensus Propagation, Moallemi C. C. and Van Roy B., IEEE Trans. Inf. Theory, 52 (2006) 4753) where the average is available for read-out at every single node. In the case that the underlying network is constant but the values to be averaged fluctuate ("dynamic data"), convergence and accuracy are determined by the spectral properties of an associated Ruelle-Perron-Frobenius operator. For Gaussian models on Erdos-Renyi graphs, numerical computation points to a spectral gap remaining in the large- size limit, implying exceptionally good scalability. In a model where the underlying network also fluctuates ("dynamic network"), averaging is more effective than in the dynamic data case. Altogether, this implies very good performance of these methods in very large systems, and opens a new field of statistical physics of large (and dynamic) information systems.

Place, publisher, year, edition, pages
2009. Vol. 87, no 6
Keyword [en]
graphical models, propagation
Identifiers
URN: urn:nbn:se:kth:diva-18855DOI: 10.1209/0295-5075/87/68004ISI: 000270659600030Scopus ID: 2-s2.0-79051469011OAI: oai:DiVA.org:kth-18855DiVA: diva2:336902
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2010-12-13Bibliographically approved

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