{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:P5PN3H4IZMKRBIDRS36ZR2PUV7","short_pith_number":"pith:P5PN3H4I","schema_version":"1.0","canonical_sha256":"7f5edd9f88cb1510a07196fd98e9f4aff627051a10ca8850ff626e86f5d78d52","source":{"kind":"arxiv","id":"2606.23435","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Analysis Using a Constrained Mixture of Normal-Inverse-Gamma Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Andr\\'es F. Barrientos, Garritt L. Page, Jonathan R. Bradley, Madelyn Clinch","submitted_at":"2026-06-22T14:55:38Z","abstract_excerpt":"Gaussian mixtures of regressions are commonly implemented via a Gibbs sampler. This Markov chain Monte Carlo (MCMC) algorithm can be computationally burdensome because of the need to update discrete-valued latent component allocation parameters whose dimension increases as the sample size increases. In this article, we propose applying the method of composition to a Gaussian finite mixture model with a Normal-Inverse-Gamma (NIG) prior which allows one to write the posterior distribution as the product of conditional distributions. Namely, the conditional distribution of parameters given the da"},"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":"2606.23435","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-06-22T14:55:38Z","cross_cats_sorted":[],"title_canon_sha256":"06696897caaec7f10b0ff8e75b7013ecf2b7c909d500033a54d90ce974c32eb8","abstract_canon_sha256":"068e7bf98df5c999b4d5e36a9dbe817921c2cb70a29b4c5b081fc345091781b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:14:19.934932Z","signature_b64":"GrS5FAzGaR+xjmYuqlIAgD3j+Y6gFIrceKWRT0n4o82keLg52soyidCi4i+C9s51/B4ovLvF9YUPT+VmyoouDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f5edd9f88cb1510a07196fd98e9f4aff627051a10ca8850ff626e86f5d78d52","last_reissued_at":"2026-06-23T03:14:19.934583Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:14:19.934583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Analysis Using a Constrained Mixture of Normal-Inverse-Gamma Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Andr\\'es F. Barrientos, Garritt L. Page, Jonathan R. Bradley, Madelyn Clinch","submitted_at":"2026-06-22T14:55:38Z","abstract_excerpt":"Gaussian mixtures of regressions are commonly implemented via a Gibbs sampler. This Markov chain Monte Carlo (MCMC) algorithm can be computationally burdensome because of the need to update discrete-valued latent component allocation parameters whose dimension increases as the sample size increases. In this article, we propose applying the method of composition to a Gaussian finite mixture model with a Normal-Inverse-Gamma (NIG) prior which allows one to write the posterior distribution as the product of conditional distributions. Namely, the conditional distribution of parameters given the da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.23435","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.23435/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2606.23435","created_at":"2026-06-23T03:14:19.934645+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.23435v1","created_at":"2026-06-23T03:14:19.934645+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.23435","created_at":"2026-06-23T03:14:19.934645+00:00"},{"alias_kind":"pith_short_12","alias_value":"P5PN3H4IZMKR","created_at":"2026-06-23T03:14:19.934645+00:00"},{"alias_kind":"pith_short_16","alias_value":"P5PN3H4IZMKRBIDR","created_at":"2026-06-23T03:14:19.934645+00:00"},{"alias_kind":"pith_short_8","alias_value":"P5PN3H4I","created_at":"2026-06-23T03:14:19.934645+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/P5PN3H4IZMKRBIDRS36ZR2PUV7","json":"https://pith.science/pith/P5PN3H4IZMKRBIDRS36ZR2PUV7.json","graph_json":"https://pith.science/api/pith-number/P5PN3H4IZMKRBIDRS36ZR2PUV7/graph.json","events_json":"https://pith.science/api/pith-number/P5PN3H4IZMKRBIDRS36ZR2PUV7/events.json","paper":"https://pith.science/paper/P5PN3H4I"},"agent_actions":{"view_html":"https://pith.science/pith/P5PN3H4IZMKRBIDRS36ZR2PUV7","download_json":"https://pith.science/pith/P5PN3H4IZMKRBIDRS36ZR2PUV7.json","view_paper":"https://pith.science/paper/P5PN3H4I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.23435&json=true","fetch_graph":"https://pith.science/api/pith-number/P5PN3H4IZMKRBIDRS36ZR2PUV7/graph.json","fetch_events":"https://pith.science/api/pith-number/P5PN3H4IZMKRBIDRS36ZR2PUV7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P5PN3H4IZMKRBIDRS36ZR2PUV7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P5PN3H4IZMKRBIDRS36ZR2PUV7/action/storage_attestation","attest_author":"https://pith.science/pith/P5PN3H4IZMKRBIDRS36ZR2PUV7/action/author_attestation","sign_citation":"https://pith.science/pith/P5PN3H4IZMKRBIDRS36ZR2PUV7/action/citation_signature","submit_replication":"https://pith.science/pith/P5PN3H4IZMKRBIDRS36ZR2PUV7/action/replication_record"}},"created_at":"2026-06-23T03:14:19.934645+00:00","updated_at":"2026-06-23T03:14:19.934645+00:00"}