{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:226CP727UCTPBP3R4CEORQNZ3H","short_pith_number":"pith:226CP727","schema_version":"1.0","canonical_sha256":"d6bc27ff5fa0a6f0bf71e088e8c1b9d9e65a9b8ef3eb12612cefa1449c667fe2","source":{"kind":"arxiv","id":"2404.12294","version":3},"attestation_state":"computed","paper":{"title":"Bayesian evidence estimation from posterior samples with normalizing flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO","cs.LG","gr-qc"],"primary_cat":"stat.ML","authors_text":"Enrico Barausse, Marco Crisostomi, Matteo Breschi, Rahul Srinivasan, Roberto Trotta","submitted_at":"2024-04-18T16:16:02Z","abstract_excerpt":"We propose a novel method ($floZ$), based on normalizing flows, to estimate the Bayesian evidence (and its numerical uncertainty) from a pre-existing set of samples drawn from the unnormalized posterior distribution. We validate it on distributions whose evidence is known analytically, up to 15 parameter space dimensions, and compare with two state-of-the-art techniques for estimating the evidence: nested sampling (which computes the evidence as its main target) and a $k$-nearest-neighbors technique that produces evidence estimates from posterior samples. Provided representative samples from t"},"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":"2404.12294","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-04-18T16:16:02Z","cross_cats_sorted":["astro-ph.CO","cs.LG","gr-qc"],"title_canon_sha256":"8b4f2a47d205ae6e327dd8ca2f29244ac5d3218cf25def340f63328879e020f0","abstract_canon_sha256":"2ce0c4925ac220032ac3ece954c35fafa2686bb1c0cfa865652c57417cf6534a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:50.433551Z","signature_b64":"8ARW30CEGW38ef5w4znnnQjgsGTpl/J/5l6vYqxg4U9GZHPvBBn5JaLyGXZOOmtEyd2K02lRC2K+1U7pdHi6Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6bc27ff5fa0a6f0bf71e088e8c1b9d9e65a9b8ef3eb12612cefa1449c667fe2","last_reissued_at":"2026-07-05T09:44:50.433013Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:50.433013Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian evidence estimation from posterior samples with normalizing flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO","cs.LG","gr-qc"],"primary_cat":"stat.ML","authors_text":"Enrico Barausse, Marco Crisostomi, Matteo Breschi, Rahul Srinivasan, Roberto Trotta","submitted_at":"2024-04-18T16:16:02Z","abstract_excerpt":"We propose a novel method ($floZ$), based on normalizing flows, to estimate the Bayesian evidence (and its numerical uncertainty) from a pre-existing set of samples drawn from the unnormalized posterior distribution. We validate it on distributions whose evidence is known analytically, up to 15 parameter space dimensions, and compare with two state-of-the-art techniques for estimating the evidence: nested sampling (which computes the evidence as its main target) and a $k$-nearest-neighbors technique that produces evidence estimates from posterior samples. Provided representative samples from t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.12294","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2404.12294/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":"2404.12294","created_at":"2026-07-05T09:44:50.433072+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.12294v3","created_at":"2026-07-05T09:44:50.433072+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.12294","created_at":"2026-07-05T09:44:50.433072+00:00"},{"alias_kind":"pith_short_12","alias_value":"226CP727UCTP","created_at":"2026-07-05T09:44:50.433072+00:00"},{"alias_kind":"pith_short_16","alias_value":"226CP727UCTPBP3R","created_at":"2026-07-05T09:44:50.433072+00:00"},{"alias_kind":"pith_short_8","alias_value":"226CP727","created_at":"2026-07-05T09:44:50.433072+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01436","citing_title":"Constraints on Einstein-aether gravity from the precision timing of PSR J1738+0333","ref_index":72,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H","json":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H.json","graph_json":"https://pith.science/api/pith-number/226CP727UCTPBP3R4CEORQNZ3H/graph.json","events_json":"https://pith.science/api/pith-number/226CP727UCTPBP3R4CEORQNZ3H/events.json","paper":"https://pith.science/paper/226CP727"},"agent_actions":{"view_html":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H","download_json":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H.json","view_paper":"https://pith.science/paper/226CP727","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.12294&json=true","fetch_graph":"https://pith.science/api/pith-number/226CP727UCTPBP3R4CEORQNZ3H/graph.json","fetch_events":"https://pith.science/api/pith-number/226CP727UCTPBP3R4CEORQNZ3H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H/action/storage_attestation","attest_author":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H/action/author_attestation","sign_citation":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H/action/citation_signature","submit_replication":"https://pith.science/pith/226CP727UCTPBP3R4CEORQNZ3H/action/replication_record"}},"created_at":"2026-07-05T09:44:50.433072+00:00","updated_at":"2026-07-05T09:44:50.433072+00:00"}