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Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing
Politecnico di Torino, Italy.
Chalmers University of Technology, Sweden.
Technical University of Madrid, Spain. (Signal Processing Applications Group)
2011 (English)In: Proc. of IEEE Statistical Signal Processing Workshop (SSP), 2011, 733-736 p.Conference paper, Poster (Refereed)
Abstract [en]

Belief propagation (BP) is a technique for distributed inference in wireless networks and is often used even when the underlying graphical model contains cycles. In this paper, we propose a uniformly reweighted BP scheme that reduces the impact of cycles by weighting messages by a constant “edge appearance probability” ρ ≤ 1. We apply this algorithm to distributed binary hypothesis testing problems (e.g., distributed detection) in wireless networks with Markov random field models. We demonstrate that in the considered setting the proposed method outperforms standard BP, while maintaining similar complexity. We then show that the optimal ρ can be approximated as a simple function of the average node degree, and can hence be computed in a distributed fashion through a consensus algorithm.

Place, publisher, year, edition, pages
2011. 733-736 p.
Keyword [en]
Belief propagation, distributed Bayesian hypothesis testing, graphical models
National Category
Engineering and Technology Signal Processing Communication Systems
URN: urn:nbn:se:liu:diva-81322DOI: 10.1109/SSP.2011.5967807ISBN: 978-1-4577-0569-4OAI: diva2:551608
IEEE Statistical Signal Processing Workshop (SSP), Nice, France
Available from: 2012-09-11 Created: 2012-09-11 Last updated: 2012-09-21

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Savic, Vladimir
Engineering and TechnologySignal ProcessingCommunication Systems

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