Uncertainty in Artificial Intelligence
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Random Algorithms for the Loop Cutset Problem
Ann Becker, Reuven Bar-Yehuada, Dan Geiger
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 ``Repeated WGuessI", outputs a minimum loop cutset, after O(c 6^k k n) steps, with probability at least 1-(1 over{6^k})^{c 6^k}), 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.
Pages: 49-56
PS Link:
PDF Link: /papers/99/p49-becker.pdf
AUTHOR = "Ann Becker and Reuven Bar-Yehuada and Dan Geiger",
TITLE = "Random Algorithms for the Loop Cutset Problem",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "49--56"

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