Approximation Algorithms for the Loop Cutset Problem
نویسندگان
چکیده
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioi~ing for inference. Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the number of variables contained in a minimum loop cutset. We test MGA on randomly generated graphs and find that the average ratio between the number of instances associated with the algorithms’ output and the number of instances associated with a minimum solution is 1.22.
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We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the first :itep in the method of conditioning for inference. Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the number of variables contained in a minimum loop cutset. The algorithm is based on a reduction to the ...
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