TY - GEN
T1 - Residual belief propagation
T2 - 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
AU - Elidan, Gal
AU - McGraw, Ian
AU - Koller, Daphne
PY - 2006
Y1 - 2006
N2 - Inference for probabilistic graphical models is still very much a practical challenge in large domains. The commonly used and effective belief propagation (BP) algorithm and its generalizations often do not converge when applied to hard, real-life inference tasks. While it is widely recognized that the scheduling of messages in these algorithms may have significant consequences, this issue remains largely unexplored. In this work, we address the question of how to schedule messages for asynchronous propagation so that a fixed point is reached faster and more often. We first show that any reasonable asynchronous BP converges to a unique fixed point under conditions similar to those that guarantee convergence of synchronous BP. In addition, we show that the convergence rate of a simple round-robin schedule is at least as good as that of synchronous propagation. We then propose residual belief propagation (RBP), a novel, easy-to-implement, asynchronous propagation algorithm that schedules messages in an informed way, that pushes down a bound on the distance from the fixed point. Finally, we demonstrate the superiority of RBP over state-of-the-art methods for a variety of challenging synthetic and real-life problems: RBP converges significantly more often than other methods; and it significantly reduces running time until convergence, even when other methods converge.
AB - Inference for probabilistic graphical models is still very much a practical challenge in large domains. The commonly used and effective belief propagation (BP) algorithm and its generalizations often do not converge when applied to hard, real-life inference tasks. While it is widely recognized that the scheduling of messages in these algorithms may have significant consequences, this issue remains largely unexplored. In this work, we address the question of how to schedule messages for asynchronous propagation so that a fixed point is reached faster and more often. We first show that any reasonable asynchronous BP converges to a unique fixed point under conditions similar to those that guarantee convergence of synchronous BP. In addition, we show that the convergence rate of a simple round-robin schedule is at least as good as that of synchronous propagation. We then propose residual belief propagation (RBP), a novel, easy-to-implement, asynchronous propagation algorithm that schedules messages in an informed way, that pushes down a bound on the distance from the fixed point. Finally, we demonstrate the superiority of RBP over state-of-the-art methods for a variety of challenging synthetic and real-life problems: RBP converges significantly more often than other methods; and it significantly reduces running time until convergence, even when other methods converge.
UR - http://www.scopus.com/inward/record.url?scp=77958077320&partnerID=8YFLogxK
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AN - SCOPUS:77958077320
SN - 0974903922
SN - 9780974903927
T3 - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
SP - 165
EP - 173
BT - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Y2 - 13 July 2006 through 16 July 2006
ER -