{"paper":{"title":"Random Algorithms for the Loop Cutset Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.AI","authors_text":"Ann Becker, Dan Geiger, Reuven Bar-Yehuada","submitted_at":"2014-08-07T06:25:05Z","abstract_excerpt":"We show how to find a minimum loop cutset in a Bayesian network with high probability.  Finding such a loop cutset is the first step in Pearl's method of conditioning for inference.  Our random algorithm for finding a loop cutset, called \"Repeated WGuessI\", outputs a minimum loop cutset, after O(c 6^k k n) steps, with probability at least 1-(1 over{6^k})^{c 6^k}), where c>1 is a constant specified by the user, k is the size of a minimum weight loop cutset, and n is the number of vertices.  We also show empirically that a variant of this algorithm, called WRA, often finds a loop cutset that is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.1483","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}