Random Algorithms for the Loop Cutset Problem
نویسندگان
چکیده
We show how to find a minimum loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in Pearl's method of conditioning for inference. Our random algorithm for finding a loop cutset, called REPEATEDWGUESSI, outputs a minimum loop cutset, after O(c.6"n) steps, with probability a t least 1 (1 $ ) c ~ * , where c > 1 is a constant specified by the user, k is the size of a minimum weight loop cutset, and n is the number of vertices. We also show empirically that a variant of this algorithm, called WRA, often finds a loop cutset that is closer to the minimum loop cutset than the ones found by the best deterministic algorithms known.
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