{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3SPBDE75W66C5X4B55UF4IZGRC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2b0b247dc8bb13883dd32a65ede0f97c39e8bddfe18ab5599d2f23d1505b33f0","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-05-11T11:35:33Z","title_canon_sha256":"b34dc3dc7bad8ab8fb1ce879f10bf0efebd3aa8efb8a50f6685bbb823ad0593d"},"schema_version":"1.0","source":{"id":"2205.05416","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.05416","created_at":"2026-07-05T04:22:25Z"},{"alias_kind":"arxiv_version","alias_value":"2205.05416v1","created_at":"2026-07-05T04:22:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.05416","created_at":"2026-07-05T04:22:25Z"},{"alias_kind":"pith_short_12","alias_value":"3SPBDE75W66C","created_at":"2026-07-05T04:22:25Z"},{"alias_kind":"pith_short_16","alias_value":"3SPBDE75W66C5X4B","created_at":"2026-07-05T04:22:25Z"},{"alias_kind":"pith_short_8","alias_value":"3SPBDE75","created_at":"2026-07-05T04:22:25Z"}],"graph_snapshots":[{"event_id":"sha256:705000e6bf6525a04c65f52bf896dbdfeb3502292ae569f0d1bea34aae5b91d8","target":"graph","created_at":"2026-07-05T04:22:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2205.05416/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Estimating the model evidence - or mariginal likelihood of the data - is a notoriously difficult task for finite and infinite mixture models and we reexamine here different Monte Carlo techniques advocated in the recent literature, as well as novel approaches based on Geyer (1994) reverse logistic regression technique, Chib (1995) algorithm, and Sequential Monte Carlo (SMC). Applications are numerous. In particular, testing for the number of components in a finite mixture model or against the fit of a finite mixture model for a given dataset has long been and still is an issue of much interest","authors_text":"Adrien Hairault, Christian P. Robert, Judith Rousseau","cross_cats":["math.ST","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-05-11T11:35:33Z","title":"Evidence estimation in finite and infinite mixture models and applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.05416","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:40de64ebb6e8ae63af8d877f778c9fc9678744a06b77c6458d85fea73380a7f7","target":"record","created_at":"2026-07-05T04:22:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2b0b247dc8bb13883dd32a65ede0f97c39e8bddfe18ab5599d2f23d1505b33f0","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-05-11T11:35:33Z","title_canon_sha256":"b34dc3dc7bad8ab8fb1ce879f10bf0efebd3aa8efb8a50f6685bbb823ad0593d"},"schema_version":"1.0","source":{"id":"2205.05416","kind":"arxiv","version":1}},"canonical_sha256":"dc9e1193fdb7bc2edf81ef685e232688be4f36a99981779a7fe9648fada48e58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dc9e1193fdb7bc2edf81ef685e232688be4f36a99981779a7fe9648fada48e58","first_computed_at":"2026-07-05T04:22:25.614094Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:22:25.614094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N36/CHocD/hSzB9gdswt0w8haZI14PoE6XcflWq2liV52WuJrkWB+R+k4QxEYCJtN4rMgBMSwNS/MNCFyc7DAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:22:25.614502Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.05416","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:40de64ebb6e8ae63af8d877f778c9fc9678744a06b77c6458d85fea73380a7f7","sha256:705000e6bf6525a04c65f52bf896dbdfeb3502292ae569f0d1bea34aae5b91d8"],"state_sha256":"6d0624c6cd2dc282a521507c1426fafffe548831e5398c8b35e9e393b3fe458e"}