{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:L5WDSHNUJAEXHWFYQXF3G2J6RD","short_pith_number":"pith:L5WDSHNU","schema_version":"1.0","canonical_sha256":"5f6c391db4480973d8b885cbb3693e88dded9e10a2332810a336b8b990266da3","source":{"kind":"arxiv","id":"1807.00070","version":3},"attestation_state":"computed","paper":{"title":"Quasi Markov Chain Monte Carlo Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","stat.TH"],"primary_cat":"math.ST","authors_text":"Ben Calderhead, Tobias Schwedes","submitted_at":"2018-06-29T21:11:50Z","abstract_excerpt":"Quasi-Monte Carlo (QMC) methods for estimating integrals are attractive since the resulting estimators typically converge at a faster rate than pseudo-random Monte Carlo. However, they can be difficult to set up on arbitrary posterior densities within the Bayesian framework, in particular for inverse problems. We introduce a general parallel Markov chain Monte Carlo (MCMC) framework, for which we prove a law of large numbers and a central limit theorem. In that context, non-reversible transitions are investigated. We then extend this approach to the use of adaptive kernels and state conditions"},"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":"1807.00070","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-06-29T21:11:50Z","cross_cats_sorted":["math.PR","stat.TH"],"title_canon_sha256":"8c61e72b43362b50a6d0d973a0f38168c772e2e169ee8969cf65f7d1d69988da","abstract_canon_sha256":"df685f4ea29f3093d0ee27f6209f5dfe62491e3ec4cc31514f0b8fc368671b7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:22.698697Z","signature_b64":"LqIyub4KLbB0R04myCXDQKTNvvNHV/wdjmC5CLe0qEnmUbE2PHNowbKi76DiXTdXyOZ2XQI0HvzVWEpsG/DyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f6c391db4480973d8b885cbb3693e88dded9e10a2332810a336b8b990266da3","last_reissued_at":"2026-05-18T00:04:22.698089Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:22.698089Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quasi Markov Chain Monte Carlo Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","stat.TH"],"primary_cat":"math.ST","authors_text":"Ben Calderhead, Tobias Schwedes","submitted_at":"2018-06-29T21:11:50Z","abstract_excerpt":"Quasi-Monte Carlo (QMC) methods for estimating integrals are attractive since the resulting estimators typically converge at a faster rate than pseudo-random Monte Carlo. However, they can be difficult to set up on arbitrary posterior densities within the Bayesian framework, in particular for inverse problems. We introduce a general parallel Markov chain Monte Carlo (MCMC) framework, for which we prove a law of large numbers and a central limit theorem. In that context, non-reversible transitions are investigated. We then extend this approach to the use of adaptive kernels and state conditions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00070","kind":"arxiv","version":3},"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":"1807.00070","created_at":"2026-05-18T00:04:22.698206+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.00070v3","created_at":"2026-05-18T00:04:22.698206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00070","created_at":"2026-05-18T00:04:22.698206+00:00"},{"alias_kind":"pith_short_12","alias_value":"L5WDSHNUJAEX","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"L5WDSHNUJAEXHWFY","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"L5WDSHNU","created_at":"2026-05-18T12:32:33.847187+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/L5WDSHNUJAEXHWFYQXF3G2J6RD","json":"https://pith.science/pith/L5WDSHNUJAEXHWFYQXF3G2J6RD.json","graph_json":"https://pith.science/api/pith-number/L5WDSHNUJAEXHWFYQXF3G2J6RD/graph.json","events_json":"https://pith.science/api/pith-number/L5WDSHNUJAEXHWFYQXF3G2J6RD/events.json","paper":"https://pith.science/paper/L5WDSHNU"},"agent_actions":{"view_html":"https://pith.science/pith/L5WDSHNUJAEXHWFYQXF3G2J6RD","download_json":"https://pith.science/pith/L5WDSHNUJAEXHWFYQXF3G2J6RD.json","view_paper":"https://pith.science/paper/L5WDSHNU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.00070&json=true","fetch_graph":"https://pith.science/api/pith-number/L5WDSHNUJAEXHWFYQXF3G2J6RD/graph.json","fetch_events":"https://pith.science/api/pith-number/L5WDSHNUJAEXHWFYQXF3G2J6RD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L5WDSHNUJAEXHWFYQXF3G2J6RD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L5WDSHNUJAEXHWFYQXF3G2J6RD/action/storage_attestation","attest_author":"https://pith.science/pith/L5WDSHNUJAEXHWFYQXF3G2J6RD/action/author_attestation","sign_citation":"https://pith.science/pith/L5WDSHNUJAEXHWFYQXF3G2J6RD/action/citation_signature","submit_replication":"https://pith.science/pith/L5WDSHNUJAEXHWFYQXF3G2J6RD/action/replication_record"}},"created_at":"2026-05-18T00:04:22.698206+00:00","updated_at":"2026-05-18T00:04:22.698206+00:00"}