{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:MOG5ZMEECDDBUDYT77KZ5NNXRY","short_pith_number":"pith:MOG5ZMEE","schema_version":"1.0","canonical_sha256":"638ddcb08410c61a0f13ffd59eb5b78e0e990de42127a89b413412d200af2cfb","source":{"kind":"arxiv","id":"1406.1411","version":2},"attestation_state":"computed","paper":{"title":"Advances in Learning Bayesian Networks of Bounded Treewidth","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Cassio Polpo de Campos, Denis Deratani Maua, Qiang Ji, Siqi Nie","submitted_at":"2014-06-05T15:10:40Z","abstract_excerpt":"This work presents novel algorithms for learning Bayesian network structures with bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed-integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in uniformly sampling $k$-trees (maximal graphs of treewidth $k$), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that $k$-tree. Some properties of these methods are discussed and proven. The approaches are empirically compared to eac"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1406.1411","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-06-05T15:10:40Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"87f0b5d330150bfb6d9c0d44b88aaeb30259b2f357bfd11172a9c6bc0e2b699c","abstract_canon_sha256":"f66ba69573329ae1d887c02f2a2becaa585e58ef21f89508b5d6ac5bb10d4475"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:50:22.423437Z","signature_b64":"uBWOX9ADVZB90rZv5vuiWN264x5LlqEW9OE9fbfJ/V00WSN/sPbWeRjKa4wdQSU6nFKzligwxR0gb9vjBOUbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"638ddcb08410c61a0f13ffd59eb5b78e0e990de42127a89b413412d200af2cfb","last_reissued_at":"2026-05-18T02:50:22.422931Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:50:22.422931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advances in Learning Bayesian Networks of Bounded Treewidth","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Cassio Polpo de Campos, Denis Deratani Maua, Qiang Ji, Siqi Nie","submitted_at":"2014-06-05T15:10:40Z","abstract_excerpt":"This work presents novel algorithms for learning Bayesian network structures with bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed-integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in uniformly sampling $k$-trees (maximal graphs of treewidth $k$), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that $k$-tree. Some properties of these methods are discussed and proven. The approaches are empirically compared to eac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.1411","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1406.1411","created_at":"2026-05-18T02:50:22.422995+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.1411v2","created_at":"2026-05-18T02:50:22.422995+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.1411","created_at":"2026-05-18T02:50:22.422995+00:00"},{"alias_kind":"pith_short_12","alias_value":"MOG5ZMEECDDB","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_16","alias_value":"MOG5ZMEECDDBUDYT","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_8","alias_value":"MOG5ZMEE","created_at":"2026-05-18T12:28:38.356838+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY","json":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY.json","graph_json":"https://pith.science/api/pith-number/MOG5ZMEECDDBUDYT77KZ5NNXRY/graph.json","events_json":"https://pith.science/api/pith-number/MOG5ZMEECDDBUDYT77KZ5NNXRY/events.json","paper":"https://pith.science/paper/MOG5ZMEE"},"agent_actions":{"view_html":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY","download_json":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY.json","view_paper":"https://pith.science/paper/MOG5ZMEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.1411&json=true","fetch_graph":"https://pith.science/api/pith-number/MOG5ZMEECDDBUDYT77KZ5NNXRY/graph.json","fetch_events":"https://pith.science/api/pith-number/MOG5ZMEECDDBUDYT77KZ5NNXRY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/action/storage_attestation","attest_author":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/action/author_attestation","sign_citation":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/action/citation_signature","submit_replication":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/action/replication_record"}},"created_at":"2026-05-18T02:50:22.422995+00:00","updated_at":"2026-05-18T02:50:22.422995+00:00"}