{"paper":{"title":"Generating Bayesian Networks from Probability Logic Knowledge Bases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Peter Haddawy","submitted_at":"2013-02-27T14:16:25Z","abstract_excerpt":"We present a method for dynamically generating Bayesian networks from knowledge bases consisting of first-order probability logic sentences.  We present a subset of probability logic sufficient for representing the class of Bayesian networks with discrete-valued nodes.  We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information necessary to generate a network.  We define the concept of d-separation for knowledge bases and prove that a knowledge base with independence conditions defined by d-separation is a complete speci"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.6811","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"}