{"paper":{"title":"Randomized Algorithms for the Loop Cutset Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"A. Becker, D. Geiger, R. Bar-Yehuda","submitted_at":"2011-06-01T16:17:38Z","abstract_excerpt":"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 close"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.0225","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"}