{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:S5WIW6HQDGADIO32ANP2XSGFA3","short_pith_number":"pith:S5WIW6HQ","schema_version":"1.0","canonical_sha256":"976c8b78f01980343b7a035fabc8c506c247e0ba111b9c5ff1e38f10a279fbd5","source":{"kind":"arxiv","id":"1702.03628","version":1},"attestation_state":"computed","paper":{"title":"Multilevel Monte Carlo in Approximate Bayesian Computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Ajay Jasra, Christine Shoemaker, David Nott, Raul Tempone, Seongil Jo","submitted_at":"2017-02-13T04:36:14Z","abstract_excerpt":"In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given."},"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":"1702.03628","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-02-13T04:36:14Z","cross_cats_sorted":[],"title_canon_sha256":"bba9830cc8703464526887c30b3e4e1e3ebb19d6e57d89457ba856e39be5fc12","abstract_canon_sha256":"be69f7acf3c368028d3a539bba8e3f9802d086fd28950cbe91474deda59deba8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:53.340541Z","signature_b64":"L2Vr5m5kcl3oJe6Bv0jML98fFCMIxhQFV+O+Hovca7yJLFbMgo4WSCNbqn1OzBeu3oyfPX4Dopf3NpHtBiKYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"976c8b78f01980343b7a035fabc8c506c247e0ba111b9c5ff1e38f10a279fbd5","last_reissued_at":"2026-05-18T00:50:53.340029Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:53.340029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multilevel Monte Carlo in Approximate Bayesian Computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Ajay Jasra, Christine Shoemaker, David Nott, Raul Tempone, Seongil Jo","submitted_at":"2017-02-13T04:36:14Z","abstract_excerpt":"In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.03628","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":""},"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":"1702.03628","created_at":"2026-05-18T00:50:53.340099+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.03628v1","created_at":"2026-05-18T00:50:53.340099+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.03628","created_at":"2026-05-18T00:50:53.340099+00:00"},{"alias_kind":"pith_short_12","alias_value":"S5WIW6HQDGAD","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"S5WIW6HQDGADIO32","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"S5WIW6HQ","created_at":"2026-05-18T12:31:43.269735+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/S5WIW6HQDGADIO32ANP2XSGFA3","json":"https://pith.science/pith/S5WIW6HQDGADIO32ANP2XSGFA3.json","graph_json":"https://pith.science/api/pith-number/S5WIW6HQDGADIO32ANP2XSGFA3/graph.json","events_json":"https://pith.science/api/pith-number/S5WIW6HQDGADIO32ANP2XSGFA3/events.json","paper":"https://pith.science/paper/S5WIW6HQ"},"agent_actions":{"view_html":"https://pith.science/pith/S5WIW6HQDGADIO32ANP2XSGFA3","download_json":"https://pith.science/pith/S5WIW6HQDGADIO32ANP2XSGFA3.json","view_paper":"https://pith.science/paper/S5WIW6HQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.03628&json=true","fetch_graph":"https://pith.science/api/pith-number/S5WIW6HQDGADIO32ANP2XSGFA3/graph.json","fetch_events":"https://pith.science/api/pith-number/S5WIW6HQDGADIO32ANP2XSGFA3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S5WIW6HQDGADIO32ANP2XSGFA3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S5WIW6HQDGADIO32ANP2XSGFA3/action/storage_attestation","attest_author":"https://pith.science/pith/S5WIW6HQDGADIO32ANP2XSGFA3/action/author_attestation","sign_citation":"https://pith.science/pith/S5WIW6HQDGADIO32ANP2XSGFA3/action/citation_signature","submit_replication":"https://pith.science/pith/S5WIW6HQDGADIO32ANP2XSGFA3/action/replication_record"}},"created_at":"2026-05-18T00:50:53.340099+00:00","updated_at":"2026-05-18T00:50:53.340099+00:00"}