{"paper":{"title":"Stochastic Logic Programs: Sampling, Inference and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"James Cussens","submitted_at":"2013-01-16T15:49:34Z","abstract_excerpt":"Algorithms for exact and approximate inference in stochastic logic   programs (SLPs) are presented, based respectively, on variable   elimination and importance sampling. We then show how SLPs can be   used to represent prior distributions for machine learning, using   (i) logic programs and (ii) Bayes net structures as examples.   Drawing on existing work in statistics, we apply the   Metropolis-Hasting algorithm to construct a Markov chain which   samples from the posterior distribution. A Prolog implementation for   this is described. We also discuss the possibility of constructing   explic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.3846","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"}