{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AZ5K6LGYIBTIJDAQASRBUNTHNV","short_pith_number":"pith:AZ5K6LGY","schema_version":"1.0","canonical_sha256":"067aaf2cd84066848c1004a21a36676d6995d3a0c8a58257e2210bc0f63cb4f3","source":{"kind":"arxiv","id":"1803.05554","version":3},"attestation_state":"computed","paper":{"title":"Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Caroline Uhler, Raj Agrawal, Tamara Broderick","submitted_at":"2018-03-15T00:53:25Z","abstract_excerpt":"Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships. However, traditional methods often fail in modern applications, which exhibit a larger number of observed variables than data points. The resulting uncertainty about the underlying network as well as the desire to incorporate prior information recommend a Bayesian approach to learning the BN, but the highly combinatorial structure of BNs poses a striking challenge for inference. The current state-of-the-art methods such as order MCMC are faster than previous methods but prevent the"},"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":"1803.05554","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2018-03-15T00:53:25Z","cross_cats_sorted":["cs.LG","stat.ME","stat.ML"],"title_canon_sha256":"5c1d51060e052c5798e601e233c13f65433ba74c801036653fde00b02059ce7a","abstract_canon_sha256":"30fcabd9d60f62b7129160b76c8ed821a5efc68df518dfc005248fc4c4ed2c7f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:31.935866Z","signature_b64":"SPcmM2X4QzebL1Szjtzm54C6K3bpCKz3I/Tqbie/frrQI3d77FUvJyYy5YF+JAeYtaU2fp57OVAtaZGs23ItAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"067aaf2cd84066848c1004a21a36676d6995d3a0c8a58257e2210bc0f63cb4f3","last_reissued_at":"2026-05-18T00:12:31.935138Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:31.935138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Caroline Uhler, Raj Agrawal, Tamara Broderick","submitted_at":"2018-03-15T00:53:25Z","abstract_excerpt":"Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships. However, traditional methods often fail in modern applications, which exhibit a larger number of observed variables than data points. The resulting uncertainty about the underlying network as well as the desire to incorporate prior information recommend a Bayesian approach to learning the BN, but the highly combinatorial structure of BNs poses a striking challenge for inference. The current state-of-the-art methods such as order MCMC are faster than previous methods but prevent the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.05554","kind":"arxiv","version":3},"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":"1803.05554","created_at":"2026-05-18T00:12:31.935265+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.05554v3","created_at":"2026-05-18T00:12:31.935265+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.05554","created_at":"2026-05-18T00:12:31.935265+00:00"},{"alias_kind":"pith_short_12","alias_value":"AZ5K6LGYIBTI","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AZ5K6LGYIBTIJDAQ","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AZ5K6LGY","created_at":"2026-05-18T12:32:13.499390+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/AZ5K6LGYIBTIJDAQASRBUNTHNV","json":"https://pith.science/pith/AZ5K6LGYIBTIJDAQASRBUNTHNV.json","graph_json":"https://pith.science/api/pith-number/AZ5K6LGYIBTIJDAQASRBUNTHNV/graph.json","events_json":"https://pith.science/api/pith-number/AZ5K6LGYIBTIJDAQASRBUNTHNV/events.json","paper":"https://pith.science/paper/AZ5K6LGY"},"agent_actions":{"view_html":"https://pith.science/pith/AZ5K6LGYIBTIJDAQASRBUNTHNV","download_json":"https://pith.science/pith/AZ5K6LGYIBTIJDAQASRBUNTHNV.json","view_paper":"https://pith.science/paper/AZ5K6LGY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.05554&json=true","fetch_graph":"https://pith.science/api/pith-number/AZ5K6LGYIBTIJDAQASRBUNTHNV/graph.json","fetch_events":"https://pith.science/api/pith-number/AZ5K6LGYIBTIJDAQASRBUNTHNV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AZ5K6LGYIBTIJDAQASRBUNTHNV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AZ5K6LGYIBTIJDAQASRBUNTHNV/action/storage_attestation","attest_author":"https://pith.science/pith/AZ5K6LGYIBTIJDAQASRBUNTHNV/action/author_attestation","sign_citation":"https://pith.science/pith/AZ5K6LGYIBTIJDAQASRBUNTHNV/action/citation_signature","submit_replication":"https://pith.science/pith/AZ5K6LGYIBTIJDAQASRBUNTHNV/action/replication_record"}},"created_at":"2026-05-18T00:12:31.935265+00:00","updated_at":"2026-05-18T00:12:31.935265+00:00"}