{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:I5ZYGBNGCLEAGQP27TOYQE6R4H","short_pith_number":"pith:I5ZYGBNG","schema_version":"1.0","canonical_sha256":"47738305a612c80341fafcdd8813d1e1cc9be31a182d0ebb003b5a5f966e9a73","source":{"kind":"arxiv","id":"1101.3266","version":2},"attestation_state":"computed","paper":{"title":"On the interpolation of univariate distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ph"],"primary_cat":"physics.data-an","authors_text":"Hans P. Dembinski","submitted_at":"2011-01-17T17:02:05Z","abstract_excerpt":"This note discusses an interpolation technique for univariate distributions. In other words, the question is how to obtain a good approximation for f(x|a) if a0 < a < a1 is a control variable and f(x|a0) and f(x|a1) are known. The technique presented here is based on the interpolation of the quantile function, i.e. the inverse of the cumulative density function."},"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":"1101.3266","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2011-01-17T17:02:05Z","cross_cats_sorted":["hep-ph"],"title_canon_sha256":"38c0c0a986ced896fba3a8baf0b225fa9bb3193e4af2f7725cbb7d76d72132ee","abstract_canon_sha256":"ccb4c29252eddb36f9e3fac1544e6780b6d483d0da1e791e2ddee115da2c761b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:29:55.257459Z","signature_b64":"w0kLqn5Es3crLBYMuivTxQkPBH2U4UrEvtgmxi3CEsWDpNe5ieA1vPq/aFsLuWF/K4PDC9oNb2dYYN6CGhFcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47738305a612c80341fafcdd8813d1e1cc9be31a182d0ebb003b5a5f966e9a73","last_reissued_at":"2026-05-18T04:29:55.256955Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:29:55.256955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the interpolation of univariate distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ph"],"primary_cat":"physics.data-an","authors_text":"Hans P. Dembinski","submitted_at":"2011-01-17T17:02:05Z","abstract_excerpt":"This note discusses an interpolation technique for univariate distributions. In other words, the question is how to obtain a good approximation for f(x|a) if a0 < a < a1 is a control variable and f(x|a0) and f(x|a1) are known. The technique presented here is based on the interpolation of the quantile function, i.e. the inverse of the cumulative density function."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1101.3266","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":"1101.3266","created_at":"2026-05-18T04:29:55.257039+00:00"},{"alias_kind":"arxiv_version","alias_value":"1101.3266v2","created_at":"2026-05-18T04:29:55.257039+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1101.3266","created_at":"2026-05-18T04:29:55.257039+00:00"},{"alias_kind":"pith_short_12","alias_value":"I5ZYGBNGCLEA","created_at":"2026-05-18T12:26:30.835961+00:00"},{"alias_kind":"pith_short_16","alias_value":"I5ZYGBNGCLEAGQP2","created_at":"2026-05-18T12:26:30.835961+00:00"},{"alias_kind":"pith_short_8","alias_value":"I5ZYGBNG","created_at":"2026-05-18T12:26:30.835961+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/I5ZYGBNGCLEAGQP27TOYQE6R4H","json":"https://pith.science/pith/I5ZYGBNGCLEAGQP27TOYQE6R4H.json","graph_json":"https://pith.science/api/pith-number/I5ZYGBNGCLEAGQP27TOYQE6R4H/graph.json","events_json":"https://pith.science/api/pith-number/I5ZYGBNGCLEAGQP27TOYQE6R4H/events.json","paper":"https://pith.science/paper/I5ZYGBNG"},"agent_actions":{"view_html":"https://pith.science/pith/I5ZYGBNGCLEAGQP27TOYQE6R4H","download_json":"https://pith.science/pith/I5ZYGBNGCLEAGQP27TOYQE6R4H.json","view_paper":"https://pith.science/paper/I5ZYGBNG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1101.3266&json=true","fetch_graph":"https://pith.science/api/pith-number/I5ZYGBNGCLEAGQP27TOYQE6R4H/graph.json","fetch_events":"https://pith.science/api/pith-number/I5ZYGBNGCLEAGQP27TOYQE6R4H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I5ZYGBNGCLEAGQP27TOYQE6R4H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I5ZYGBNGCLEAGQP27TOYQE6R4H/action/storage_attestation","attest_author":"https://pith.science/pith/I5ZYGBNGCLEAGQP27TOYQE6R4H/action/author_attestation","sign_citation":"https://pith.science/pith/I5ZYGBNGCLEAGQP27TOYQE6R4H/action/citation_signature","submit_replication":"https://pith.science/pith/I5ZYGBNGCLEAGQP27TOYQE6R4H/action/replication_record"}},"created_at":"2026-05-18T04:29:55.257039+00:00","updated_at":"2026-05-18T04:29:55.257039+00:00"}