{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6DEYGOMKYGGCXWKIFCW5534MHQ","short_pith_number":"pith:6DEYGOMK","schema_version":"1.0","canonical_sha256":"f0c983398ac18c2bd94828addeef8c3c2fe0a16106d5ffbb029110cba7e42526","source":{"kind":"arxiv","id":"2401.15645","version":2},"attestation_state":"computed","paper":{"title":"Ensemble-Based Annealed Importance Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.NA","physics.comp-ph","stat.ML"],"primary_cat":"stat.CO","authors_text":"Haoxuan Chen, Lexing Ying","submitted_at":"2024-01-28T12:47:39Z","abstract_excerpt":"Sampling from a multimodal distribution is a fundamental and challenging problem in computational science and statistics. Among various approaches proposed for this task, one popular method is Annealed Importance Sampling (AIS). In this paper, we propose an ensemble-based version of AIS by combining it with population-based Monte Carlo methods to improve its efficiency. By keeping track of an ensemble instead of a single particle along some continuation path between the starting distribution and the target distribution, we take advantage of the interaction within the ensemble to encourage the "},"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":"2401.15645","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2024-01-28T12:47:39Z","cross_cats_sorted":["cs.LG","cs.NA","math.NA","physics.comp-ph","stat.ML"],"title_canon_sha256":"910d275014415ac5f4d16c2c0d663fec0bf897d821eaab80aa49d05b5ae46cbc","abstract_canon_sha256":"8834072cfafd9691c741225cbf4cdeb80da4b18ba3af18b472b198c2e9c3107c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:31:43.252433Z","signature_b64":"XencHbD2/zCtEA/U92c72X67Whh+hZBdWHFnlqxE3NxgEkmu0umTGKmtAsw8h2DVK3gPquOurWZvOXacMnxiDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0c983398ac18c2bd94828addeef8c3c2fe0a16106d5ffbb029110cba7e42526","last_reissued_at":"2026-07-05T09:31:43.251935Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:31:43.251935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ensemble-Based Annealed Importance Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.NA","physics.comp-ph","stat.ML"],"primary_cat":"stat.CO","authors_text":"Haoxuan Chen, Lexing Ying","submitted_at":"2024-01-28T12:47:39Z","abstract_excerpt":"Sampling from a multimodal distribution is a fundamental and challenging problem in computational science and statistics. Among various approaches proposed for this task, one popular method is Annealed Importance Sampling (AIS). In this paper, we propose an ensemble-based version of AIS by combining it with population-based Monte Carlo methods to improve its efficiency. By keeping track of an ensemble instead of a single particle along some continuation path between the starting distribution and the target distribution, we take advantage of the interaction within the ensemble to encourage the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.15645","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.15645/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":"2401.15645","created_at":"2026-07-05T09:31:43.252011+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.15645v2","created_at":"2026-07-05T09:31:43.252011+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.15645","created_at":"2026-07-05T09:31:43.252011+00:00"},{"alias_kind":"pith_short_12","alias_value":"6DEYGOMKYGGC","created_at":"2026-07-05T09:31:43.252011+00:00"},{"alias_kind":"pith_short_16","alias_value":"6DEYGOMKYGGCXWKI","created_at":"2026-07-05T09:31:43.252011+00:00"},{"alias_kind":"pith_short_8","alias_value":"6DEYGOMK","created_at":"2026-07-05T09:31:43.252011+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.04575","citing_title":"Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ","json":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ.json","graph_json":"https://pith.science/api/pith-number/6DEYGOMKYGGCXWKIFCW5534MHQ/graph.json","events_json":"https://pith.science/api/pith-number/6DEYGOMKYGGCXWKIFCW5534MHQ/events.json","paper":"https://pith.science/paper/6DEYGOMK"},"agent_actions":{"view_html":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ","download_json":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ.json","view_paper":"https://pith.science/paper/6DEYGOMK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.15645&json=true","fetch_graph":"https://pith.science/api/pith-number/6DEYGOMKYGGCXWKIFCW5534MHQ/graph.json","fetch_events":"https://pith.science/api/pith-number/6DEYGOMKYGGCXWKIFCW5534MHQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ/action/storage_attestation","attest_author":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ/action/author_attestation","sign_citation":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ/action/citation_signature","submit_replication":"https://pith.science/pith/6DEYGOMKYGGCXWKIFCW5534MHQ/action/replication_record"}},"created_at":"2026-07-05T09:31:43.252011+00:00","updated_at":"2026-07-05T09:31:43.252011+00:00"}