{"paper":{"title":"An Empirical Study of w-Cutset Sampling for Bayesian Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bozhena Bidyuk, Rina Dechter","submitted_at":"2012-10-19T15:03:43Z","abstract_excerpt":"The paper studies empirically the time-space trade-off between sampling and     inference in a sl cutset sampling algorithm. The algorithm samples over a     subset of nodes in a Bayesian network and applies exact inference over the     rest. Consequently, while the size of the sampling space decreases, requiring     less samples for convergence, the time for generating each single sample     increases. The w-cutset sampling selects a sampling set such that the     induced-width of the network when the sampling set is observed is bounded by w,     thus requiring inference whose complexity is e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2449","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"}