{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:FR6IXJEDDTEM2DXW5HBU5I5H73","short_pith_number":"pith:FR6IXJED","schema_version":"1.0","canonical_sha256":"2c7c8ba4831cc8cd0ef6e9c34ea3a7fefc67da3d19f1835449567d95f55c2d7c","source":{"kind":"arxiv","id":"1510.06307","version":2},"attestation_state":"computed","paper":{"title":"Bayesian Nonparametric Density Estimation under Length Bias","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Spyridon J. Hatjispyros, Stephen G. Walker, Theodoros Nicoleris","submitted_at":"2015-10-21T15:46:58Z","abstract_excerpt":"A density estimation method in a Bayesian nonparametric framework is presented when recorded data are not coming directly from the distribution of interest, but from a length biased version. From a Bayesian perspective, efforts to computationally evaluate posterior quantities conditionally on length biased data were hindered by the inability to circumvent the problem of a normalizing constant. In this paper we present a novel Bayesian nonparametric approach to the length bias sampling problem which circumvents the issue of the normalizing constant. Numerical illustrations as well as a real dat"},"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":"1510.06307","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-10-21T15:46:58Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"6bc7c77edeb759fa178beb8d7048835618a50a821696f29a1e47ae18c064a475","abstract_canon_sha256":"530aba720971b14079b893a123a4e805bbdb89c8c0fddaf586c85824f3c804e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:29:31.124830Z","signature_b64":"paWELfd5h1kODWMs0cas9DDEicB/O4K6ZR4/CPfhi4Ld4QyvG81YhwCRtfbOtauFnHe0AHvaldf2xuI97XTZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c7c8ba4831cc8cd0ef6e9c34ea3a7fefc67da3d19f1835449567d95f55c2d7c","last_reissued_at":"2026-05-18T01:29:31.124294Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:29:31.124294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Nonparametric Density Estimation under Length Bias","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Spyridon J. Hatjispyros, Stephen G. Walker, Theodoros Nicoleris","submitted_at":"2015-10-21T15:46:58Z","abstract_excerpt":"A density estimation method in a Bayesian nonparametric framework is presented when recorded data are not coming directly from the distribution of interest, but from a length biased version. From a Bayesian perspective, efforts to computationally evaluate posterior quantities conditionally on length biased data were hindered by the inability to circumvent the problem of a normalizing constant. In this paper we present a novel Bayesian nonparametric approach to the length bias sampling problem which circumvents the issue of the normalizing constant. Numerical illustrations as well as a real dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.06307","kind":"arxiv","version":2},"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":"1510.06307","created_at":"2026-05-18T01:29:31.124379+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.06307v2","created_at":"2026-05-18T01:29:31.124379+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.06307","created_at":"2026-05-18T01:29:31.124379+00:00"},{"alias_kind":"pith_short_12","alias_value":"FR6IXJEDDTEM","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"FR6IXJEDDTEM2DXW","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"FR6IXJED","created_at":"2026-05-18T12:29:22.688609+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/FR6IXJEDDTEM2DXW5HBU5I5H73","json":"https://pith.science/pith/FR6IXJEDDTEM2DXW5HBU5I5H73.json","graph_json":"https://pith.science/api/pith-number/FR6IXJEDDTEM2DXW5HBU5I5H73/graph.json","events_json":"https://pith.science/api/pith-number/FR6IXJEDDTEM2DXW5HBU5I5H73/events.json","paper":"https://pith.science/paper/FR6IXJED"},"agent_actions":{"view_html":"https://pith.science/pith/FR6IXJEDDTEM2DXW5HBU5I5H73","download_json":"https://pith.science/pith/FR6IXJEDDTEM2DXW5HBU5I5H73.json","view_paper":"https://pith.science/paper/FR6IXJED","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.06307&json=true","fetch_graph":"https://pith.science/api/pith-number/FR6IXJEDDTEM2DXW5HBU5I5H73/graph.json","fetch_events":"https://pith.science/api/pith-number/FR6IXJEDDTEM2DXW5HBU5I5H73/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FR6IXJEDDTEM2DXW5HBU5I5H73/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FR6IXJEDDTEM2DXW5HBU5I5H73/action/storage_attestation","attest_author":"https://pith.science/pith/FR6IXJEDDTEM2DXW5HBU5I5H73/action/author_attestation","sign_citation":"https://pith.science/pith/FR6IXJEDDTEM2DXW5HBU5I5H73/action/citation_signature","submit_replication":"https://pith.science/pith/FR6IXJEDDTEM2DXW5HBU5I5H73/action/replication_record"}},"created_at":"2026-05-18T01:29:31.124379+00:00","updated_at":"2026-05-18T01:29:31.124379+00:00"}