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Abstract: We showhow to find a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in the method of conditioning for inference. Our randomized algorithm for finding a loop cutset outputs a minimum loop cutset after O(c 6 k kn) steps with probability at least 1 ; (1 ; 1 6 k ) c6 k , where c?1isa constant specified by the user, k is the minimal size of a minimum weight loop cutset, and n is the number of vertices. We also show... (Update)
Context of citations to this paper: More
.... time O( 917kg) n e) 14] Also there exists a randomized algorithm which needs O(c4kkn) time with probability at least i (1 ) c4k [12]. The k feedback vertex set problem (r ) asks whether an input graph has a feedback vertex set of size k. Kloks et al. 40] proved...
.... 917k ) n e) Bod92] Also there exists a randomized algorithm which needs O(c4 kn) time with probability at least 1 (1 k ) c4 [BBYG00] The k feedback vertex set problem (FVS) asks whether an input graph has a feedback vertex set of size k. Kloks et al. KLL01] proved that...
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BibTeX entry: (Update)
Ann Becker, Reuven Bar-Yehuda, and Dan Geiger. Randomized algorithms for the loop cutset problem. Journal of Artificial Intelligence Research, 12:219-- 234, 2000. http://citeseer.ist.psu.edu/becker00randomized.html More
@article{ becker00randomized,
author = "Ann Becker and Reuven Bar-Yehuda and Dan Geiger",
title = "Randomized Algorithms for the Loop Cutset Problem",
journal = "Journal of Artificial Intelligence Research",
volume = "12",
pages = "219-234",
year = "2000",
url = "citeseer.ist.psu.edu/becker00randomized.html" }
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