TY - CONF
N2 - 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? rho ? 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 ? 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.
CY - EEUU
TI - Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing
AV - public
Y1 - 2011///
M2 - Niza, Francia
T2 - 20011 IEEE of Statistical Signal Processing Workshop (SSP)
PB - IEEE
A1 - Penna, Federico
A1 - Wymeersch, Henk
A1 - Savic, Vladimir
UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5967807
SN - 978-1-4577-0569-4
ID - upm12196
ER -