{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:UIFCQZPTVGFOVDMQPPD2WLINON","short_pith_number":"pith:UIFCQZPT","schema_version":"1.0","canonical_sha256":"a20a2865f3a98aea8d907bc7ab2d0d7365aa4bab0b4f91c2c095fd5c21b28aca","source":{"kind":"arxiv","id":"1503.00996","version":2},"attestation_state":"computed","paper":{"title":"Accelerating Metropolis-Hastings algorithms by Delayed Acceptance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Anthony Lee (U. Warwick), Christian P. Robert (U. Paris-Dauphine, Clara Grazian (Sapienza Universit\\`a di Roma, Marco Banterle (U. Paris-Dauphine), U. Paris-Dauphine), U. Warwick)","submitted_at":"2015-03-03T16:28:15Z","abstract_excerpt":"MCMC algorithms such as Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions as exemplified by huge datasets. We offer in this paper a useful generalisation of the Delayed Acceptance approach, devised to reduce the computational costs of such algorithms by a simple and universal divide-and-conquer strategy. The idea behind the generic acceleration is to divide the acceptance step into several parts, aiming at a major reduction in computing time that out-ranks the corresponding reduction in acceptance probability. Each of the components can be sequen"},"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":"1503.00996","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-03-03T16:28:15Z","cross_cats_sorted":[],"title_canon_sha256":"2c1949c017fd38e13c5b6b35f9081ca08deeef0a68afbc8ba0dbecd9d7e54c40","abstract_canon_sha256":"997ca4e5037c758791669427b5bca56bd807dbd122df8fc9945244069c4d5d3e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:25:33.608632Z","signature_b64":"m3KgdHSQyCQIXfpYPze+PzuUM1kSfVI5J/e8dSRkyv7LvfrQsq0s8gR7SBbX6LNI/IuCvuRjOCyrelW+XSYFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a20a2865f3a98aea8d907bc7ab2d0d7365aa4bab0b4f91c2c095fd5c21b28aca","last_reissued_at":"2026-05-18T02:25:33.608227Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:25:33.608227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating Metropolis-Hastings algorithms by Delayed Acceptance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Anthony Lee (U. Warwick), Christian P. Robert (U. Paris-Dauphine, Clara Grazian (Sapienza Universit\\`a di Roma, Marco Banterle (U. Paris-Dauphine), U. Paris-Dauphine), U. Warwick)","submitted_at":"2015-03-03T16:28:15Z","abstract_excerpt":"MCMC algorithms such as Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions as exemplified by huge datasets. We offer in this paper a useful generalisation of the Delayed Acceptance approach, devised to reduce the computational costs of such algorithms by a simple and universal divide-and-conquer strategy. The idea behind the generic acceleration is to divide the acceptance step into several parts, aiming at a major reduction in computing time that out-ranks the corresponding reduction in acceptance probability. Each of the components can be sequen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.00996","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":"1503.00996","created_at":"2026-05-18T02:25:33.608289+00:00"},{"alias_kind":"arxiv_version","alias_value":"1503.00996v2","created_at":"2026-05-18T02:25:33.608289+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.00996","created_at":"2026-05-18T02:25:33.608289+00:00"},{"alias_kind":"pith_short_12","alias_value":"UIFCQZPTVGFO","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_16","alias_value":"UIFCQZPTVGFOVDMQ","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_8","alias_value":"UIFCQZPT","created_at":"2026-05-18T12:29:44.643036+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/UIFCQZPTVGFOVDMQPPD2WLINON","json":"https://pith.science/pith/UIFCQZPTVGFOVDMQPPD2WLINON.json","graph_json":"https://pith.science/api/pith-number/UIFCQZPTVGFOVDMQPPD2WLINON/graph.json","events_json":"https://pith.science/api/pith-number/UIFCQZPTVGFOVDMQPPD2WLINON/events.json","paper":"https://pith.science/paper/UIFCQZPT"},"agent_actions":{"view_html":"https://pith.science/pith/UIFCQZPTVGFOVDMQPPD2WLINON","download_json":"https://pith.science/pith/UIFCQZPTVGFOVDMQPPD2WLINON.json","view_paper":"https://pith.science/paper/UIFCQZPT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1503.00996&json=true","fetch_graph":"https://pith.science/api/pith-number/UIFCQZPTVGFOVDMQPPD2WLINON/graph.json","fetch_events":"https://pith.science/api/pith-number/UIFCQZPTVGFOVDMQPPD2WLINON/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UIFCQZPTVGFOVDMQPPD2WLINON/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UIFCQZPTVGFOVDMQPPD2WLINON/action/storage_attestation","attest_author":"https://pith.science/pith/UIFCQZPTVGFOVDMQPPD2WLINON/action/author_attestation","sign_citation":"https://pith.science/pith/UIFCQZPTVGFOVDMQPPD2WLINON/action/citation_signature","submit_replication":"https://pith.science/pith/UIFCQZPTVGFOVDMQPPD2WLINON/action/replication_record"}},"created_at":"2026-05-18T02:25:33.608289+00:00","updated_at":"2026-05-18T02:25:33.608289+00:00"}