{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZB6L4JOGCUB3IRYAMRVMSISONE","short_pith_number":"pith:ZB6L4JOG","schema_version":"1.0","canonical_sha256":"c87cbe25c61503b44700646ac9224e690138fbbff00dd0861e034a1e7560e1f2","source":{"kind":"arxiv","id":"2505.04603","version":1},"attestation_state":"computed","paper":{"title":"Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Wenhui Sophia Lu, Wing Hung Wong","submitted_at":"2025-05-07T17:50:14Z","abstract_excerpt":"When the likelihood is analytically unavailable and computationally intractable, approximate Bayesian computation (ABC) has emerged as a widely used methodology for approximate posterior inference; however, it suffers from severe computational inefficiency in high-dimensional settings or under diffuse priors. To overcome these limitations, we propose Adaptive Bayesian Inference (ABI), a framework that bypasses traditional data-space discrepancies and instead compares distributions directly in posterior space through nonparametric distribution matching. By leveraging a novel Marginally-augmente"},"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":"2505.04603","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-05-07T17:50:14Z","cross_cats_sorted":["cs.LG","stat.CO","stat.ML"],"title_canon_sha256":"da224e136ea808442efb0ceb1de2ac518a3c37978ed56cd3e8fd0ed3d635b382","abstract_canon_sha256":"8e3e16efcf3e4c44cc214ec3363349828c2c948a2272729371d385e783b3c377"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:54.443997Z","signature_b64":"JLIbrBM81FMhPgTudhK+2RzfS7E2HssHSrVgKDyYr85oXFItyzax1vLR6lkDcASDqLsKqMVcYrHirZKVf00YDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c87cbe25c61503b44700646ac9224e690138fbbff00dd0861e034a1e7560e1f2","last_reissued_at":"2026-07-05T10:59:54.443548Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:54.443548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Wenhui Sophia Lu, Wing Hung Wong","submitted_at":"2025-05-07T17:50:14Z","abstract_excerpt":"When the likelihood is analytically unavailable and computationally intractable, approximate Bayesian computation (ABC) has emerged as a widely used methodology for approximate posterior inference; however, it suffers from severe computational inefficiency in high-dimensional settings or under diffuse priors. To overcome these limitations, we propose Adaptive Bayesian Inference (ABI), a framework that bypasses traditional data-space discrepancies and instead compares distributions directly in posterior space through nonparametric distribution matching. By leveraging a novel Marginally-augmente"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04603","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/2505.04603/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":"2505.04603","created_at":"2026-07-05T10:59:54.443605+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.04603v1","created_at":"2026-07-05T10:59:54.443605+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04603","created_at":"2026-07-05T10:59:54.443605+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZB6L4JOGCUB3","created_at":"2026-07-05T10:59:54.443605+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZB6L4JOGCUB3IRYA","created_at":"2026-07-05T10:59:54.443605+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZB6L4JOG","created_at":"2026-07-05T10:59:54.443605+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/ZB6L4JOGCUB3IRYAMRVMSISONE","json":"https://pith.science/pith/ZB6L4JOGCUB3IRYAMRVMSISONE.json","graph_json":"https://pith.science/api/pith-number/ZB6L4JOGCUB3IRYAMRVMSISONE/graph.json","events_json":"https://pith.science/api/pith-number/ZB6L4JOGCUB3IRYAMRVMSISONE/events.json","paper":"https://pith.science/paper/ZB6L4JOG"},"agent_actions":{"view_html":"https://pith.science/pith/ZB6L4JOGCUB3IRYAMRVMSISONE","download_json":"https://pith.science/pith/ZB6L4JOGCUB3IRYAMRVMSISONE.json","view_paper":"https://pith.science/paper/ZB6L4JOG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.04603&json=true","fetch_graph":"https://pith.science/api/pith-number/ZB6L4JOGCUB3IRYAMRVMSISONE/graph.json","fetch_events":"https://pith.science/api/pith-number/ZB6L4JOGCUB3IRYAMRVMSISONE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZB6L4JOGCUB3IRYAMRVMSISONE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZB6L4JOGCUB3IRYAMRVMSISONE/action/storage_attestation","attest_author":"https://pith.science/pith/ZB6L4JOGCUB3IRYAMRVMSISONE/action/author_attestation","sign_citation":"https://pith.science/pith/ZB6L4JOGCUB3IRYAMRVMSISONE/action/citation_signature","submit_replication":"https://pith.science/pith/ZB6L4JOGCUB3IRYAMRVMSISONE/action/replication_record"}},"created_at":"2026-07-05T10:59:54.443605+00:00","updated_at":"2026-07-05T10:59:54.443605+00:00"}