{"paper":{"title":"Dependency Networks for Collaborative Filtering and Data Visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.AI","authors_text":"Carl Kadie, Christopher Meek, David Heckerman, David Maxwell Chickering, Robert Rounthwaite","submitted_at":"2013-01-16T15:50:38Z","abstract_excerpt":"We describe a graphical model for probabilistic relationships---an alternative to the Bayesian network---called a dependency network.  The graph of a dependency network, unlike a Bayesian network, is potentially cyclic.  The probability component of a dependency network, like a Bayesian network, is a set of conditional distributions, one for each node given its parents.  We identify several basic properties of this representation and describe a computationally efficient procedure for learning the graph and probability components from data.  We describe the application of this representation to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.3862","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"}