CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing

Federico Penna ; Henk Wymeersch (Institutionen för signaler och system, Kommunikationssystem) ; Vladimir Savic
Proc. IEEE International Workshop on Statistical Signal Processing (SSP) p. 733 - 736 . (2011)
[Konferensbidrag, refereegranskat]

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.



Den här publikationen ingår i följande styrkeområden:

Läs mer om Chalmers styrkeområden  

Denna post skapades 2011-08-24. Senast ändrad 2013-08-23.
CPL Pubid: 144856

 

Läs direkt!

Lokal fulltext (fritt tillgänglig)

Länk till annan sajt (kan kräva inloggning)