ML Anthology

Approximation Algorithms for the Loop Cutset Problem

Ann Becker, Dan Geiger
UAI 1994 pp. 60-68
doi:10.1016/B978-1-55860-332-5.50013-4 /uai/1994/becker1994uai-approximation/

Abstract

We show how to find a small loop cutset 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 cut-set 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.

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Text

Becker and Geiger. "Approximation Algorithms for the Loop Cutset Problem." Conference on Uncertainty in Artificial Intelligence, 1994. doi:10.1016/B978-1-55860-332-5.50013-4

Markdown

[Becker and Geiger. "Approximation Algorithms for the Loop Cutset Problem." Conference on Uncertainty in Artificial Intelligence, 1994.](https://mlanthology.org/uai/1994/becker1994uai-approximation/) doi:10.1016/B978-1-55860-332-5.50013-4

BibTeX

@inproceedings{becker1994uai-approximation,
  title     = {{Approximation Algorithms for the Loop Cutset Problem}},
  author    = {Becker, Ann and Geiger, Dan},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1994},
  pages     = {60-68},
  doi       = {10.1016/B978-1-55860-332-5.50013-4},
  url       = {https://mlanthology.org/uai/1994/becker1994uai-approximation/}
}