Approximation Algorithms for the Loop Cutset Problem
Artificial Intelligence
2013-02-28 v1 Data Structures and Algorithms
Abstract
We show how to find a small loop curser in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioning 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 the number of instances associated with a minimum solution is 1.22.
Keywords
Cite
@article{arxiv.1302.6787,
title = {Approximation Algorithms for the Loop Cutset Problem},
author = {Ann Becker and Dan Geiger},
journal= {arXiv preprint arXiv:1302.6787},
year = {2013}
}
Comments
Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)