{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:UFRA7JNXNACA6FTND5HSD7DLDO","short_pith_number":"pith:UFRA7JNX","schema_version":"1.0","canonical_sha256":"a1620fa5b768040f166d1f4f21fc6b1b88b89fe811b030669801de6deab6e1fe","source":{"kind":"arxiv","id":"1210.4844","version":1},"attestation_state":"computed","paper":{"title":"Plackett-Luce regression: A new Bayesian model for polychotomous data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"Cedric Archambeau, Francois Caron","submitted_at":"2012-10-16T17:34:18Z","abstract_excerpt":"Multinomial logistic regression is one of the most popular models for modelling the effect of explanatory variables on a subject choice between a set of specified options. This model has found numerous applications in machine learning, psychology or economy. Bayesian inference in this model is non trivial and requires, either to resort to a MetropolisHastings algorithm, or rejection sampling within a Gibbs sampler. In this paper, we propose an alternative model to multinomial logistic regression. The model builds on the Plackett-Luce model, a popular model for multiple comparisons. We show tha"},"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":"1210.4844","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-10-16T17:34:18Z","cross_cats_sorted":["stat.AP","stat.CO"],"title_canon_sha256":"3ade8b6235efd1ddb91b2fd4476a8cba51d1971277f417a269b17a3315146222","abstract_canon_sha256":"cf914b2dfe0c1653957c3bdaf500f4c7f6cdc038070397426b7f3eb70a4c9955"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:42:55.543552Z","signature_b64":"oBZ+vt/1p7YDCRcljr/+vga2cRTUmW47t/uHUZSrF625piHBlL/HHDGLQijfZGo74XlARV3RHza0HSEkcNBLBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1620fa5b768040f166d1f4f21fc6b1b88b89fe811b030669801de6deab6e1fe","last_reissued_at":"2026-05-18T03:42:55.542851Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:42:55.542851Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Plackett-Luce regression: A new Bayesian model for polychotomous data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"Cedric Archambeau, Francois Caron","submitted_at":"2012-10-16T17:34:18Z","abstract_excerpt":"Multinomial logistic regression is one of the most popular models for modelling the effect of explanatory variables on a subject choice between a set of specified options. This model has found numerous applications in machine learning, psychology or economy. Bayesian inference in this model is non trivial and requires, either to resort to a MetropolisHastings algorithm, or rejection sampling within a Gibbs sampler. In this paper, we propose an alternative model to multinomial logistic regression. The model builds on the Plackett-Luce model, a popular model for multiple comparisons. We show tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.4844","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":"1210.4844","created_at":"2026-05-18T03:42:55.542977+00:00"},{"alias_kind":"arxiv_version","alias_value":"1210.4844v1","created_at":"2026-05-18T03:42:55.542977+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.4844","created_at":"2026-05-18T03:42:55.542977+00:00"},{"alias_kind":"pith_short_12","alias_value":"UFRA7JNXNACA","created_at":"2026-05-18T12:27:23.164592+00:00"},{"alias_kind":"pith_short_16","alias_value":"UFRA7JNXNACA6FTN","created_at":"2026-05-18T12:27:23.164592+00:00"},{"alias_kind":"pith_short_8","alias_value":"UFRA7JNX","created_at":"2026-05-18T12:27:23.164592+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/UFRA7JNXNACA6FTND5HSD7DLDO","json":"https://pith.science/pith/UFRA7JNXNACA6FTND5HSD7DLDO.json","graph_json":"https://pith.science/api/pith-number/UFRA7JNXNACA6FTND5HSD7DLDO/graph.json","events_json":"https://pith.science/api/pith-number/UFRA7JNXNACA6FTND5HSD7DLDO/events.json","paper":"https://pith.science/paper/UFRA7JNX"},"agent_actions":{"view_html":"https://pith.science/pith/UFRA7JNXNACA6FTND5HSD7DLDO","download_json":"https://pith.science/pith/UFRA7JNXNACA6FTND5HSD7DLDO.json","view_paper":"https://pith.science/paper/UFRA7JNX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1210.4844&json=true","fetch_graph":"https://pith.science/api/pith-number/UFRA7JNXNACA6FTND5HSD7DLDO/graph.json","fetch_events":"https://pith.science/api/pith-number/UFRA7JNXNACA6FTND5HSD7DLDO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UFRA7JNXNACA6FTND5HSD7DLDO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UFRA7JNXNACA6FTND5HSD7DLDO/action/storage_attestation","attest_author":"https://pith.science/pith/UFRA7JNXNACA6FTND5HSD7DLDO/action/author_attestation","sign_citation":"https://pith.science/pith/UFRA7JNXNACA6FTND5HSD7DLDO/action/citation_signature","submit_replication":"https://pith.science/pith/UFRA7JNXNACA6FTND5HSD7DLDO/action/replication_record"}},"created_at":"2026-05-18T03:42:55.542977+00:00","updated_at":"2026-05-18T03:42:55.542977+00:00"}