{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:D262VPXYI42MJVAQCBRDODTDRC","short_pith_number":"pith:D262VPXY","schema_version":"1.0","canonical_sha256":"1ebdaabef84734c4d4101062370e6388b95fdcc64c3b28cfedd17eeaa3f18d0b","source":{"kind":"arxiv","id":"1710.01702","version":4},"attestation_state":"computed","paper":{"title":"A Bayesian hierarchical model for related densities using Polya trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jonathan Christensen, Li Ma","submitted_at":"2017-10-04T17:11:54Z","abstract_excerpt":"Bayesian hierarchical models are used to share information between related samples and obtain more accurate estimates of sample-level parameters, common structure, and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. A number of such models have been described in the literature using extensions of the Dirichlet process and related processes, typically as a distribution on the parameters of a mixing kernel. We propose a new hierarchical model based on the P\\'olya tree"},"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":"1710.01702","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-04T17:11:54Z","cross_cats_sorted":[],"title_canon_sha256":"1c46e2b29e3c0ea55bb147c584bc4e59d69448558d861d00de5d8df788185505","abstract_canon_sha256":"d1b64c5f514ba08c6902c20f2b72d96103dca5c9e978e5633f3a74db2709586b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:18.462896Z","signature_b64":"DQb6ZAHmEQgSysPP60yEraXzcMihMcUFnv9/9zSsCfa1/0TCNmEYcOQkFjhtaFxdR2PA/cH+2Za3744dv4ixCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ebdaabef84734c4d4101062370e6388b95fdcc64c3b28cfedd17eeaa3f18d0b","last_reissued_at":"2026-05-17T23:43:18.462247Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:18.462247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Bayesian hierarchical model for related densities using Polya trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jonathan Christensen, Li Ma","submitted_at":"2017-10-04T17:11:54Z","abstract_excerpt":"Bayesian hierarchical models are used to share information between related samples and obtain more accurate estimates of sample-level parameters, common structure, and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. A number of such models have been described in the literature using extensions of the Dirichlet process and related processes, typically as a distribution on the parameters of a mixing kernel. We propose a new hierarchical model based on the P\\'olya tree"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.01702","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":"1710.01702","created_at":"2026-05-17T23:43:18.462325+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.01702v4","created_at":"2026-05-17T23:43:18.462325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.01702","created_at":"2026-05-17T23:43:18.462325+00:00"},{"alias_kind":"pith_short_12","alias_value":"D262VPXYI42M","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"D262VPXYI42MJVAQ","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"D262VPXY","created_at":"2026-05-18T12:31:10.602751+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/D262VPXYI42MJVAQCBRDODTDRC","json":"https://pith.science/pith/D262VPXYI42MJVAQCBRDODTDRC.json","graph_json":"https://pith.science/api/pith-number/D262VPXYI42MJVAQCBRDODTDRC/graph.json","events_json":"https://pith.science/api/pith-number/D262VPXYI42MJVAQCBRDODTDRC/events.json","paper":"https://pith.science/paper/D262VPXY"},"agent_actions":{"view_html":"https://pith.science/pith/D262VPXYI42MJVAQCBRDODTDRC","download_json":"https://pith.science/pith/D262VPXYI42MJVAQCBRDODTDRC.json","view_paper":"https://pith.science/paper/D262VPXY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.01702&json=true","fetch_graph":"https://pith.science/api/pith-number/D262VPXYI42MJVAQCBRDODTDRC/graph.json","fetch_events":"https://pith.science/api/pith-number/D262VPXYI42MJVAQCBRDODTDRC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D262VPXYI42MJVAQCBRDODTDRC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D262VPXYI42MJVAQCBRDODTDRC/action/storage_attestation","attest_author":"https://pith.science/pith/D262VPXYI42MJVAQCBRDODTDRC/action/author_attestation","sign_citation":"https://pith.science/pith/D262VPXYI42MJVAQCBRDODTDRC/action/citation_signature","submit_replication":"https://pith.science/pith/D262VPXYI42MJVAQCBRDODTDRC/action/replication_record"}},"created_at":"2026-05-17T23:43:18.462325+00:00","updated_at":"2026-05-17T23:43:18.462325+00:00"}