{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:44ZWKK3SGLLSOZVEK6ERIH76WN","short_pith_number":"pith:44ZWKK3S","schema_version":"1.0","canonical_sha256":"e733652b7232d72766a45789141ffeb34f40873a1648ec23358ba655ce84388a","source":{"kind":"arxiv","id":"1409.3795","version":4},"attestation_state":"computed","paper":{"title":"On the correspondence from Bayesian log-linear modelling to logistic regression modelling with $g$-priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Michail Papathomas","submitted_at":"2014-09-12T17:13:03Z","abstract_excerpt":"Consider a set of categorical variables where at least one of them is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific logistic regression model, with the binary variable as the outcome. Within the Bayesian framework, the $g$-prior and mixtures of $g$-priors are commonly assigned to the parameters of a generalized linear model. We prove that assigning a $g$-prior (or a mixture of $g$-priors) to the parameters of a certain log-linear model designates a $g$-prior (or a mixture of $g$-priors) on the parameters of the corresponding logist"},"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":"1409.3795","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-09-12T17:13:03Z","cross_cats_sorted":[],"title_canon_sha256":"43fcc5054c11baf63c42ae8584c91fd842af816d388df2680eb510103f4942f5","abstract_canon_sha256":"7800ca8c4c6f6fca1e97a7ecfef3759b176d233dabb3c287b361c6999eb4fe69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:04.333089Z","signature_b64":"YFPonsGOdtN91uQwqXNilqCR+KhLX3uz63uQRlLqsZRnIuW1xpNKfAEqB/zH9pgobvODjp3uIgHxmCkVa29fDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e733652b7232d72766a45789141ffeb34f40873a1648ec23358ba655ce84388a","last_reissued_at":"2026-05-18T00:45:04.332724Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:04.332724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the correspondence from Bayesian log-linear modelling to logistic regression modelling with $g$-priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Michail Papathomas","submitted_at":"2014-09-12T17:13:03Z","abstract_excerpt":"Consider a set of categorical variables where at least one of them is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific logistic regression model, with the binary variable as the outcome. Within the Bayesian framework, the $g$-prior and mixtures of $g$-priors are commonly assigned to the parameters of a generalized linear model. We prove that assigning a $g$-prior (or a mixture of $g$-priors) to the parameters of a certain log-linear model designates a $g$-prior (or a mixture of $g$-priors) on the parameters of the corresponding logist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.3795","kind":"arxiv","version":4},"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":"1409.3795","created_at":"2026-05-18T00:45:04.332780+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.3795v4","created_at":"2026-05-18T00:45:04.332780+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.3795","created_at":"2026-05-18T00:45:04.332780+00:00"},{"alias_kind":"pith_short_12","alias_value":"44ZWKK3SGLLS","created_at":"2026-05-18T12:28:14.216126+00:00"},{"alias_kind":"pith_short_16","alias_value":"44ZWKK3SGLLSOZVE","created_at":"2026-05-18T12:28:14.216126+00:00"},{"alias_kind":"pith_short_8","alias_value":"44ZWKK3S","created_at":"2026-05-18T12:28:14.216126+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/44ZWKK3SGLLSOZVEK6ERIH76WN","json":"https://pith.science/pith/44ZWKK3SGLLSOZVEK6ERIH76WN.json","graph_json":"https://pith.science/api/pith-number/44ZWKK3SGLLSOZVEK6ERIH76WN/graph.json","events_json":"https://pith.science/api/pith-number/44ZWKK3SGLLSOZVEK6ERIH76WN/events.json","paper":"https://pith.science/paper/44ZWKK3S"},"agent_actions":{"view_html":"https://pith.science/pith/44ZWKK3SGLLSOZVEK6ERIH76WN","download_json":"https://pith.science/pith/44ZWKK3SGLLSOZVEK6ERIH76WN.json","view_paper":"https://pith.science/paper/44ZWKK3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.3795&json=true","fetch_graph":"https://pith.science/api/pith-number/44ZWKK3SGLLSOZVEK6ERIH76WN/graph.json","fetch_events":"https://pith.science/api/pith-number/44ZWKK3SGLLSOZVEK6ERIH76WN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/44ZWKK3SGLLSOZVEK6ERIH76WN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/44ZWKK3SGLLSOZVEK6ERIH76WN/action/storage_attestation","attest_author":"https://pith.science/pith/44ZWKK3SGLLSOZVEK6ERIH76WN/action/author_attestation","sign_citation":"https://pith.science/pith/44ZWKK3SGLLSOZVEK6ERIH76WN/action/citation_signature","submit_replication":"https://pith.science/pith/44ZWKK3SGLLSOZVEK6ERIH76WN/action/replication_record"}},"created_at":"2026-05-18T00:45:04.332780+00:00","updated_at":"2026-05-18T00:45:04.332780+00:00"}