Randomized Algorithms for the Loop Cutset Problem
Artificial Intelligence
2011-06-02 v1
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 6^k kn) steps with probability at least 1 - (1 - 1/(6^k))^c6^k, 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.
Cite
@article{arxiv.1106.0225,
title = {Randomized Algorithms for the Loop Cutset Problem},
author = {R. Bar-Yehuda and A. Becker and D. Geiger},
journal= {arXiv preprint arXiv:1106.0225},
year = {2011}
}