Sample-and-accumulate algorithms for belief updating in Bayes networks

Authors:
Eugene Santos, Jr.;Solomon Eyal Shimony;Edward Williams
Affiliations:
Dept. of Elec. and Comp. Eng., Air Force Inst. of Tech., Wright-Patterson AFB, OH;Dept. of Math. and Comp. Sci., Ben Gurion University of the Negev, Beer-Sheva, Israel;Dept. of Elec. and Comp. Eng., Air Force Inst. of Tech., Wright-Patterson AFB, OH
Venue:
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Year:
1996

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Abstract

Belief updating in Bayes nets, a well known computationally hard problem, has recently been approximated by several deterministic algorithms, and by various randomized approximation algorithlns. Deterministic algorithms usually provide probability bounds, but have an exponential runtime. Some randomized schemes have, a polynomial runtime, but provide only probability estimates. We present randomized algorithms that enumerate high-probability partial instantiations, resulting in probability bounds. Some of these algorithms are also sampling algorithms. Specifically, we introduce and evalu ate a variant of backward sampling, both as a sampling algorithm and as a randomized enumeration algorithm. We also relax the implicit assumption used by both sampling and accumulation algorithms, that query nodes must be instantiated in all the samples.