{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:76E4FWXXE7J7GNF6WHCBDOYPFC","short_pith_number":"pith:76E4FWXX","schema_version":"1.0","canonical_sha256":"ff89c2daf727d3f334beb1c411bb0f28b5e65590e760c190b141eac8368f1f3b","source":{"kind":"arxiv","id":"1404.1429","version":3},"attestation_state":"computed","paper":{"title":"Nonparametric Bayes inference on conditional independence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"David B. Dunson, Tsuyoshi Kunihama","submitted_at":"2014-04-05T03:59:29Z","abstract_excerpt":"In broad applications, it is routinely of interest to assess whether there is evidence in the data to refute the assumption of conditional independence of $Y$ and $X$ conditionally on $Z$. Such tests are well developed in parametric models but are not straightforward in the nonparametric case. We propose a general Bayesian approach, which relies on an encompassing nonparametric Bayes model for the joint distribution of $Y$, $X$ and $Z$. The framework allows $Y$, $X$ and $Z$ to be random variables on arbitrary spaces, and can accommodate different dimensional vectors having a mixture of discret"},"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":"1404.1429","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-04-05T03:59:29Z","cross_cats_sorted":[],"title_canon_sha256":"7c8f742a11fbb73b6c61d701655918713b35fff92d396e9a035dd8a14493ab19","abstract_canon_sha256":"522889eb170a8330dc1b83cef1679dd82bccfcd40e152e23baa6671c528b2697"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:20:36.890388Z","signature_b64":"ZCCsecfaDljPnAVlks+f8Isx6P4sRHAwnNVjpleHaS5+xL9zs9Hh1YsLa6rpCYLU74/QAvfHtluG4wr1wNLZDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff89c2daf727d3f334beb1c411bb0f28b5e65590e760c190b141eac8368f1f3b","last_reissued_at":"2026-05-18T02:20:36.889667Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:20:36.889667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric Bayes inference on conditional independence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"David B. Dunson, Tsuyoshi Kunihama","submitted_at":"2014-04-05T03:59:29Z","abstract_excerpt":"In broad applications, it is routinely of interest to assess whether there is evidence in the data to refute the assumption of conditional independence of $Y$ and $X$ conditionally on $Z$. Such tests are well developed in parametric models but are not straightforward in the nonparametric case. We propose a general Bayesian approach, which relies on an encompassing nonparametric Bayes model for the joint distribution of $Y$, $X$ and $Z$. The framework allows $Y$, $X$ and $Z$ to be random variables on arbitrary spaces, and can accommodate different dimensional vectors having a mixture of discret"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.1429","kind":"arxiv","version":3},"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":"1404.1429","created_at":"2026-05-18T02:20:36.889786+00:00"},{"alias_kind":"arxiv_version","alias_value":"1404.1429v3","created_at":"2026-05-18T02:20:36.889786+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.1429","created_at":"2026-05-18T02:20:36.889786+00:00"},{"alias_kind":"pith_short_12","alias_value":"76E4FWXXE7J7","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_16","alias_value":"76E4FWXXE7J7GNF6","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_8","alias_value":"76E4FWXX","created_at":"2026-05-18T12:28:16.859392+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/76E4FWXXE7J7GNF6WHCBDOYPFC","json":"https://pith.science/pith/76E4FWXXE7J7GNF6WHCBDOYPFC.json","graph_json":"https://pith.science/api/pith-number/76E4FWXXE7J7GNF6WHCBDOYPFC/graph.json","events_json":"https://pith.science/api/pith-number/76E4FWXXE7J7GNF6WHCBDOYPFC/events.json","paper":"https://pith.science/paper/76E4FWXX"},"agent_actions":{"view_html":"https://pith.science/pith/76E4FWXXE7J7GNF6WHCBDOYPFC","download_json":"https://pith.science/pith/76E4FWXXE7J7GNF6WHCBDOYPFC.json","view_paper":"https://pith.science/paper/76E4FWXX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1404.1429&json=true","fetch_graph":"https://pith.science/api/pith-number/76E4FWXXE7J7GNF6WHCBDOYPFC/graph.json","fetch_events":"https://pith.science/api/pith-number/76E4FWXXE7J7GNF6WHCBDOYPFC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/76E4FWXXE7J7GNF6WHCBDOYPFC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/76E4FWXXE7J7GNF6WHCBDOYPFC/action/storage_attestation","attest_author":"https://pith.science/pith/76E4FWXXE7J7GNF6WHCBDOYPFC/action/author_attestation","sign_citation":"https://pith.science/pith/76E4FWXXE7J7GNF6WHCBDOYPFC/action/citation_signature","submit_replication":"https://pith.science/pith/76E4FWXXE7J7GNF6WHCBDOYPFC/action/replication_record"}},"created_at":"2026-05-18T02:20:36.889786+00:00","updated_at":"2026-05-18T02:20:36.889786+00:00"}