{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:J6H4JIIAN7QBSP6LGK7PD2TVCR","short_pith_number":"pith:J6H4JIIA","schema_version":"1.0","canonical_sha256":"4f8fc4a1006fe0193fcb32bef1ea751462da0ee25915842e5c953dd7596b3ae4","source":{"kind":"arxiv","id":"2411.12068","version":1},"attestation_state":"computed","paper":{"title":"The Statistical Accuracy of Neural Posterior and Likelihood Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.CO","stat.TH"],"primary_cat":"stat.ML","authors_text":"Christopher Drovandi, David J. Warne, David T. Frazier, Ryan Kelly","submitted_at":"2024-11-18T21:25:32Z","abstract_excerpt":"Neural posterior estimation (NPE) and neural likelihood estimation (NLE) are machine learning approaches that provide accurate posterior, and likelihood, approximations in complex modeling scenarios, and in situations where conducting amortized inference is a necessity. While such methods have shown significant promise across a range of diverse scientific applications, the statistical accuracy of these methods is so far unexplored. In this manuscript, we give, for the first time, an in-depth exploration on the statistical behavior of NPE and NLE. We prove that these methods have similar theore"},"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":"2411.12068","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-11-18T21:25:32Z","cross_cats_sorted":["cs.LG","math.ST","stat.CO","stat.TH"],"title_canon_sha256":"a340091abc3a62520fd53de55b742403c770b5db78570a3426d86dc707c2c813","abstract_canon_sha256":"818da2b10456608470a6189693204f98fb118f2d6c13d4fd910019e67738a334"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:15.631560Z","signature_b64":"JVQmdxn7s2MKW2jZGJhfoBpB+HblBvQF2/sIHiDATBJ89KIfRBi58muoZKXP60Pma5ciwqLmxZfgB0R16zPoBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f8fc4a1006fe0193fcb32bef1ea751462da0ee25915842e5c953dd7596b3ae4","last_reissued_at":"2026-07-05T09:37:15.631082Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:15.631082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Statistical Accuracy of Neural Posterior and Likelihood Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.CO","stat.TH"],"primary_cat":"stat.ML","authors_text":"Christopher Drovandi, David J. Warne, David T. Frazier, Ryan Kelly","submitted_at":"2024-11-18T21:25:32Z","abstract_excerpt":"Neural posterior estimation (NPE) and neural likelihood estimation (NLE) are machine learning approaches that provide accurate posterior, and likelihood, approximations in complex modeling scenarios, and in situations where conducting amortized inference is a necessity. While such methods have shown significant promise across a range of diverse scientific applications, the statistical accuracy of these methods is so far unexplored. In this manuscript, we give, for the first time, an in-depth exploration on the statistical behavior of NPE and NLE. We prove that these methods have similar theore"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.12068","kind":"arxiv","version":1},"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/2411.12068/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":"2411.12068","created_at":"2026-07-05T09:37:15.631139+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.12068v1","created_at":"2026-07-05T09:37:15.631139+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.12068","created_at":"2026-07-05T09:37:15.631139+00:00"},{"alias_kind":"pith_short_12","alias_value":"J6H4JIIAN7QB","created_at":"2026-07-05T09:37:15.631139+00:00"},{"alias_kind":"pith_short_16","alias_value":"J6H4JIIAN7QBSP6L","created_at":"2026-07-05T09:37:15.631139+00:00"},{"alias_kind":"pith_short_8","alias_value":"J6H4JIIA","created_at":"2026-07-05T09:37:15.631139+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06252","citing_title":"A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2508.20614","citing_title":"Improving the Accuracy of Amortized Model Comparison with Self-Consistency","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR","json":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR.json","graph_json":"https://pith.science/api/pith-number/J6H4JIIAN7QBSP6LGK7PD2TVCR/graph.json","events_json":"https://pith.science/api/pith-number/J6H4JIIAN7QBSP6LGK7PD2TVCR/events.json","paper":"https://pith.science/paper/J6H4JIIA"},"agent_actions":{"view_html":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR","download_json":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR.json","view_paper":"https://pith.science/paper/J6H4JIIA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.12068&json=true","fetch_graph":"https://pith.science/api/pith-number/J6H4JIIAN7QBSP6LGK7PD2TVCR/graph.json","fetch_events":"https://pith.science/api/pith-number/J6H4JIIAN7QBSP6LGK7PD2TVCR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR/action/storage_attestation","attest_author":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR/action/author_attestation","sign_citation":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR/action/citation_signature","submit_replication":"https://pith.science/pith/J6H4JIIAN7QBSP6LGK7PD2TVCR/action/replication_record"}},"created_at":"2026-07-05T09:37:15.631139+00:00","updated_at":"2026-07-05T09:37:15.631139+00:00"}