{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:XTGYMPCFYHBLV5PZQ5CU6567IU","short_pith_number":"pith:XTGYMPCF","schema_version":"1.0","canonical_sha256":"bccd863c45c1c2baf5f987454f77df45055fbb9ea9ab790979e9bfde206f5b61","source":{"kind":"arxiv","id":"1409.2713","version":1},"attestation_state":"computed","paper":{"title":"Context-specific independence in graphical log-linear models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Henrik Nyman, Johan Pensar, Jukka Corander, Timo Koski","submitted_at":"2014-09-09T12:33:56Z","abstract_excerpt":"Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameterization based on probabilities, leading to a powerful way of defining restrictions derived from marginal, conditional and context-specific independence. However, parameter estimation is often simpler under a direct parameterization, provided that the model enjoys certain decomposability properties. Here we introduce a cyclical projection algorithm for obtaining maximum likelihood estimates of log-linear parameters unde"},"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":"1409.2713","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-09T12:33:56Z","cross_cats_sorted":[],"title_canon_sha256":"640acf3207f8b31eb0ff4a4c7ae59aa0c214756532429999cf4e53284dce72c4","abstract_canon_sha256":"5c4b4f6f5ed7218a559d18c4ed6d9570662f86db9c999c09433005de02d50f96"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:35:51.885985Z","signature_b64":"YNZ8U5WeuJEYV+6CsJcrnLNryBP6EJQ3YR/e2GA91gomqD50Pz0s0Z+l0JOu9dXzGtBYUMAAJoB5br4ialfnAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bccd863c45c1c2baf5f987454f77df45055fbb9ea9ab790979e9bfde206f5b61","last_reissued_at":"2026-05-18T01:35:51.885213Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:35:51.885213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Context-specific independence in graphical log-linear models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Henrik Nyman, Johan Pensar, Jukka Corander, Timo Koski","submitted_at":"2014-09-09T12:33:56Z","abstract_excerpt":"Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameterization based on probabilities, leading to a powerful way of defining restrictions derived from marginal, conditional and context-specific independence. However, parameter estimation is often simpler under a direct parameterization, provided that the model enjoys certain decomposability properties. Here we introduce a cyclical projection algorithm for obtaining maximum likelihood estimates of log-linear parameters unde"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.2713","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1409.2713","created_at":"2026-05-18T01:35:51.885363+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.2713v1","created_at":"2026-05-18T01:35:51.885363+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.2713","created_at":"2026-05-18T01:35:51.885363+00:00"},{"alias_kind":"pith_short_12","alias_value":"XTGYMPCFYHBL","created_at":"2026-05-18T12:28:57.508820+00:00"},{"alias_kind":"pith_short_16","alias_value":"XTGYMPCFYHBLV5PZ","created_at":"2026-05-18T12:28:57.508820+00:00"},{"alias_kind":"pith_short_8","alias_value":"XTGYMPCF","created_at":"2026-05-18T12:28:57.508820+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/XTGYMPCFYHBLV5PZQ5CU6567IU","json":"https://pith.science/pith/XTGYMPCFYHBLV5PZQ5CU6567IU.json","graph_json":"https://pith.science/api/pith-number/XTGYMPCFYHBLV5PZQ5CU6567IU/graph.json","events_json":"https://pith.science/api/pith-number/XTGYMPCFYHBLV5PZQ5CU6567IU/events.json","paper":"https://pith.science/paper/XTGYMPCF"},"agent_actions":{"view_html":"https://pith.science/pith/XTGYMPCFYHBLV5PZQ5CU6567IU","download_json":"https://pith.science/pith/XTGYMPCFYHBLV5PZQ5CU6567IU.json","view_paper":"https://pith.science/paper/XTGYMPCF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.2713&json=true","fetch_graph":"https://pith.science/api/pith-number/XTGYMPCFYHBLV5PZQ5CU6567IU/graph.json","fetch_events":"https://pith.science/api/pith-number/XTGYMPCFYHBLV5PZQ5CU6567IU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XTGYMPCFYHBLV5PZQ5CU6567IU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XTGYMPCFYHBLV5PZQ5CU6567IU/action/storage_attestation","attest_author":"https://pith.science/pith/XTGYMPCFYHBLV5PZQ5CU6567IU/action/author_attestation","sign_citation":"https://pith.science/pith/XTGYMPCFYHBLV5PZQ5CU6567IU/action/citation_signature","submit_replication":"https://pith.science/pith/XTGYMPCFYHBLV5PZQ5CU6567IU/action/replication_record"}},"created_at":"2026-05-18T01:35:51.885363+00:00","updated_at":"2026-05-18T01:35:51.885363+00:00"}