{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:MOG5ZMEECDDBUDYT77KZ5NNXRY","short_pith_number":"pith:MOG5ZMEE","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"},"canonical_sha256":"638ddcb08410c61a0f13ffd59eb5b78e0e990de42127a89b413412d200af2cfb","source":{"kind":"arxiv","id":"1406.1411","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1406.1411","created_at":"2026-05-18T02:50:22Z"},{"alias_kind":"arxiv_version","alias_value":"1406.1411v2","created_at":"2026-05-18T02:50:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.1411","created_at":"2026-05-18T02:50:22Z"},{"alias_kind":"pith_short_12","alias_value":"MOG5ZMEECDDB","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_16","alias_value":"MOG5ZMEECDDBUDYT","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_8","alias_value":"MOG5ZMEE","created_at":"2026-05-18T12:28:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:MOG5ZMEECDDBUDYT77KZ5NNXRY","target":"record","payload":{"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"},"canonical_sha256":"638ddcb08410c61a0f13ffd59eb5b78e0e990de42127a89b413412d200af2cfb","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"},"source_kind":"arxiv","source_id":"1406.1411","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:50:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U4Zx/4aJPx3MQGA3//tupqDSt7qQnxBZ1vL3RW3i2mDc+J2y4Eg+eIbCRcn8nZ/0o+GK9mwZKzPwVWO9otbhDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T03:00:39.979415Z"},"content_sha256":"57b81434b7e595fe3f00c4016549884f1420c6970533bce47c92b60a39cb0ab5","schema_version":"1.0","event_id":"sha256:57b81434b7e595fe3f00c4016549884f1420c6970533bce47c92b60a39cb0ab5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:MOG5ZMEECDDBUDYT77KZ5NNXRY","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:50:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MYTSKc4IqJOY5zLNOzcmpzhreLqEDQgkKgO8m5cPesMoAY+axRTR0i/blgMVbjKUgAcxjmCqut8R2VS4w3deBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T03:00:39.979777Z"},"content_sha256":"7fb668858c1c17628ba924e194baa9bb343109882dccc41b1854204e462d6ee8","schema_version":"1.0","event_id":"sha256:7fb668858c1c17628ba924e194baa9bb343109882dccc41b1854204e462d6ee8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/bundle.json","state_url":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-28T03:00:39Z","links":{"resolver":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY","bundle":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/bundle.json","state":"https://pith.science/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MOG5ZMEECDDBUDYT77KZ5NNXRY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:MOG5ZMEECDDBUDYT77KZ5NNXRY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f66ba69573329ae1d887c02f2a2becaa585e58ef21f89508b5d6ac5bb10d4475","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-06-05T15:10:40Z","title_canon_sha256":"87f0b5d330150bfb6d9c0d44b88aaeb30259b2f357bfd11172a9c6bc0e2b699c"},"schema_version":"1.0","source":{"id":"1406.1411","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1406.1411","created_at":"2026-05-18T02:50:22Z"},{"alias_kind":"arxiv_version","alias_value":"1406.1411v2","created_at":"2026-05-18T02:50:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.1411","created_at":"2026-05-18T02:50:22Z"},{"alias_kind":"pith_short_12","alias_value":"MOG5ZMEECDDB","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_16","alias_value":"MOG5ZMEECDDBUDYT","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_8","alias_value":"MOG5ZMEE","created_at":"2026-05-18T12:28:38Z"}],"graph_snapshots":[{"event_id":"sha256:7fb668858c1c17628ba924e194baa9bb343109882dccc41b1854204e462d6ee8","target":"graph","created_at":"2026-05-18T02:50:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"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","authors_text":"Cassio Polpo de Campos, Denis Deratani Maua, Qiang Ji, Siqi Nie","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-06-05T15:10:40Z","title":"Advances in Learning Bayesian Networks of Bounded Treewidth"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.1411","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:57b81434b7e595fe3f00c4016549884f1420c6970533bce47c92b60a39cb0ab5","target":"record","created_at":"2026-05-18T02:50:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f66ba69573329ae1d887c02f2a2becaa585e58ef21f89508b5d6ac5bb10d4475","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-06-05T15:10:40Z","title_canon_sha256":"87f0b5d330150bfb6d9c0d44b88aaeb30259b2f357bfd11172a9c6bc0e2b699c"},"schema_version":"1.0","source":{"id":"1406.1411","kind":"arxiv","version":2}},"canonical_sha256":"638ddcb08410c61a0f13ffd59eb5b78e0e990de42127a89b413412d200af2cfb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"638ddcb08410c61a0f13ffd59eb5b78e0e990de42127a89b413412d200af2cfb","first_computed_at":"2026-05-18T02:50:22.422931Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:50:22.422931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uBWOX9ADVZB90rZv5vuiWN264x5LlqEW9OE9fbfJ/V00WSN/sPbWeRjKa4wdQSU6nFKzligwxR0gb9vjBOUbDw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:50:22.423437Z","signed_message":"canonical_sha256_bytes"},"source_id":"1406.1411","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:57b81434b7e595fe3f00c4016549884f1420c6970533bce47c92b60a39cb0ab5","sha256:7fb668858c1c17628ba924e194baa9bb343109882dccc41b1854204e462d6ee8"],"state_sha256":"47f9a51ecc90485c74194e303653ecec1579ff9f8d4c466c8a37b5cdb274626d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lU+T59lYDWTZE3wc90nGnvQm9VPOVX8lbVG5MTOdE1HghSNV8UCaTnLYKmzwSogT2QfOXv4yryWZRThf7Q2PBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T03:00:39.981820Z","bundle_sha256":"0547429d23d2c6055a14cd4dd4c53ff3184c21f1417d86fa1da63ccd977a16af"}}