Randomized algorithms for the loop cutset problem

  • Authors:
  • Ann Becker;Reuven Bar-Yehuda;Dan Geiger

  • Affiliations:
  • Computer Science Department, Technion, Haifa, Israel;Computer Science Department, Technion, Haifa, Israel;Computer Science Department, Technion, Haifa, Israel

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 2000

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Abstract

We show how 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 6kkn) steps with probability at least 1 - (1 - 1/6k)c6k, where c 1 is a 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 empirically that a variant of this algorithm often finds a loop cutset that is closer to the minimum weight loop cutset than the ones found by the best deterministic algorithms known.