Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing
2011 (English)In: Proc. of IEEE Statistical Signal Processing Workshop (SSP), 2011, 733-736 p.Conference paper, Poster (Refereed)
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.
Belief propagation, distributed Bayesian hypothesis testing, graphical models
Engineering and Technology Signal Processing Communication Systems
IdentifiersURN: urn:nbn:se:liu:diva-81322DOI: 10.1109/SSP.2011.5967807ISBN: 978-1-4577-0569-4OAI: oai:DiVA.org:liu-81322DiVA: diva2:551608
IEEE Statistical Signal Processing Workshop (SSP), Nice, France