{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:LQPYNXSTQLMQV5QNQU65TNMNOA","short_pith_number":"pith:LQPYNXST","schema_version":"1.0","canonical_sha256":"5c1f86de5382d90af60d853dd9b58d70216f7acd693043429fc0c327c3f5f3ce","source":{"kind":"arxiv","id":"1507.02608","version":6},"attestation_state":"computed","paper":{"title":"High-dimensional consistency in score-based and hybrid structure learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Alain Hauser, Marloes H. Maathuis, Preetam Nandy","submitted_at":"2015-07-09T17:31:52Z","abstract_excerpt":"Main approaches for learning Bayesian networks can be classified as constraint-based, score-based or hybrid methods. Although high-dimensional consistency results are available for constraint-based methods like the PC algorithm, such results have not been proved for score-based or hybrid methods, and most of the hybrid methods have not even shown to be consistent in the classical setting where the number of variables remains fixed and the sample size tends to infinity. In this paper, we show that consistency of hybrid methods based on greedy equivalence search (GES) can be achieved in the clas"},"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":"1507.02608","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-07-09T17:31:52Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"edd04a1b1c326d1c75c1e0316a97e957779fca38ccceb1a61699eb12d201812e","abstract_canon_sha256":"03bf7ad8fed18487955c71fe40f6f0431992871fcd519cc882d6666771f38d19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:32.020407Z","signature_b64":"9B3wcRU3JCN4Gp3BxA3E76flH7MvwrOl98rqt+pxJwN1OUQ3Pc9qR3tY6Zk7GBpWt2de0iZEe7n+gxuGClCzAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c1f86de5382d90af60d853dd9b58d70216f7acd693043429fc0c327c3f5f3ce","last_reissued_at":"2026-05-18T00:24:32.019920Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:32.019920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High-dimensional consistency in score-based and hybrid structure learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Alain Hauser, Marloes H. Maathuis, Preetam Nandy","submitted_at":"2015-07-09T17:31:52Z","abstract_excerpt":"Main approaches for learning Bayesian networks can be classified as constraint-based, score-based or hybrid methods. Although high-dimensional consistency results are available for constraint-based methods like the PC algorithm, such results have not been proved for score-based or hybrid methods, and most of the hybrid methods have not even shown to be consistent in the classical setting where the number of variables remains fixed and the sample size tends to infinity. In this paper, we show that consistency of hybrid methods based on greedy equivalence search (GES) can be achieved in the clas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.02608","kind":"arxiv","version":6},"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":"1507.02608","created_at":"2026-05-18T00:24:32.019996+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.02608v6","created_at":"2026-05-18T00:24:32.019996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.02608","created_at":"2026-05-18T00:24:32.019996+00:00"},{"alias_kind":"pith_short_12","alias_value":"LQPYNXSTQLMQ","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_16","alias_value":"LQPYNXSTQLMQV5QN","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_8","alias_value":"LQPYNXST","created_at":"2026-05-18T12:29:29.992203+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/LQPYNXSTQLMQV5QNQU65TNMNOA","json":"https://pith.science/pith/LQPYNXSTQLMQV5QNQU65TNMNOA.json","graph_json":"https://pith.science/api/pith-number/LQPYNXSTQLMQV5QNQU65TNMNOA/graph.json","events_json":"https://pith.science/api/pith-number/LQPYNXSTQLMQV5QNQU65TNMNOA/events.json","paper":"https://pith.science/paper/LQPYNXST"},"agent_actions":{"view_html":"https://pith.science/pith/LQPYNXSTQLMQV5QNQU65TNMNOA","download_json":"https://pith.science/pith/LQPYNXSTQLMQV5QNQU65TNMNOA.json","view_paper":"https://pith.science/paper/LQPYNXST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.02608&json=true","fetch_graph":"https://pith.science/api/pith-number/LQPYNXSTQLMQV5QNQU65TNMNOA/graph.json","fetch_events":"https://pith.science/api/pith-number/LQPYNXSTQLMQV5QNQU65TNMNOA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LQPYNXSTQLMQV5QNQU65TNMNOA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LQPYNXSTQLMQV5QNQU65TNMNOA/action/storage_attestation","attest_author":"https://pith.science/pith/LQPYNXSTQLMQV5QNQU65TNMNOA/action/author_attestation","sign_citation":"https://pith.science/pith/LQPYNXSTQLMQV5QNQU65TNMNOA/action/citation_signature","submit_replication":"https://pith.science/pith/LQPYNXSTQLMQV5QNQU65TNMNOA/action/replication_record"}},"created_at":"2026-05-18T00:24:32.019996+00:00","updated_at":"2026-05-18T00:24:32.019996+00:00"}