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Approximation Algorithms for the Loop Cutset Problem
[chapter]
1994
Uncertainty Proceedings 1994
Preserved Fulltext
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
doi:10.1016/b978-1-55860-332-5.50013-4
fatcat:o7ziypl57jei7dtgjhqqr4qqbi