{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:QGLDK7V5GVVHAPKZSH4AOZI5NU","short_pith_number":"pith:QGLDK7V5","schema_version":"1.0","canonical_sha256":"8196357ebd356a703d5991f807651d6d36e5db0c6a42ee7c52a9a69de0f8e8f7","source":{"kind":"arxiv","id":"1710.04382","version":1},"attestation_state":"computed","paper":{"title":"Marginal sequential Monte Carlo for doubly intractable models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.data-an","stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Dennis Prangle, Mark Bell, Philip Maybank, Richard G. Everitt","submitted_at":"2017-10-12T06:36:14Z","abstract_excerpt":"Bayesian inference for models that have an intractable partition function is known as a doubly intractable problem, where standard Monte Carlo methods are not applicable. The past decade has seen the development of auxiliary variable Monte Carlo techniques (M{\\o}ller et al., 2006; Murray et al., 2006) for tackling this problem; these approaches being members of the more general class of pseudo-marginal, or exact-approximate, Monte Carlo algorithms (Andrieu and Roberts, 2009), which make use of unbiased estimates of intractable posteriors. Everitt et al. (2017) investigated the use of exact-app"},"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":"1710.04382","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-10-12T06:36:14Z","cross_cats_sorted":["cs.AI","physics.data-an","stat.ME","stat.ML"],"title_canon_sha256":"8d38de8eccdbea8532a7f93f3069bcb996732d22690830158304402815227ba1","abstract_canon_sha256":"3e519c460d119023b5649ef6f7e8d357c92fa1afc0edace0f1c4b64233ac4d23"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:01.222064Z","signature_b64":"zMHpxQjjJl26K0Kmn4/u70rLjjdD8LMj8hR3NxEA2JRFmdu0bNpDEAnXv8tpbKcIew0iArlxTnlp/KrvgkyTDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8196357ebd356a703d5991f807651d6d36e5db0c6a42ee7c52a9a69de0f8e8f7","last_reissued_at":"2026-05-18T00:33:01.221485Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:01.221485Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Marginal sequential Monte Carlo for doubly intractable models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.data-an","stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Dennis Prangle, Mark Bell, Philip Maybank, Richard G. Everitt","submitted_at":"2017-10-12T06:36:14Z","abstract_excerpt":"Bayesian inference for models that have an intractable partition function is known as a doubly intractable problem, where standard Monte Carlo methods are not applicable. The past decade has seen the development of auxiliary variable Monte Carlo techniques (M{\\o}ller et al., 2006; Murray et al., 2006) for tackling this problem; these approaches being members of the more general class of pseudo-marginal, or exact-approximate, Monte Carlo algorithms (Andrieu and Roberts, 2009), which make use of unbiased estimates of intractable posteriors. Everitt et al. (2017) investigated the use of exact-app"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04382","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":"1710.04382","created_at":"2026-05-18T00:33:01.221577+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04382v1","created_at":"2026-05-18T00:33:01.221577+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04382","created_at":"2026-05-18T00:33:01.221577+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGLDK7V5GVVH","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGLDK7V5GVVHAPKZ","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGLDK7V5","created_at":"2026-05-18T12:31:37.085036+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/QGLDK7V5GVVHAPKZSH4AOZI5NU","json":"https://pith.science/pith/QGLDK7V5GVVHAPKZSH4AOZI5NU.json","graph_json":"https://pith.science/api/pith-number/QGLDK7V5GVVHAPKZSH4AOZI5NU/graph.json","events_json":"https://pith.science/api/pith-number/QGLDK7V5GVVHAPKZSH4AOZI5NU/events.json","paper":"https://pith.science/paper/QGLDK7V5"},"agent_actions":{"view_html":"https://pith.science/pith/QGLDK7V5GVVHAPKZSH4AOZI5NU","download_json":"https://pith.science/pith/QGLDK7V5GVVHAPKZSH4AOZI5NU.json","view_paper":"https://pith.science/paper/QGLDK7V5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04382&json=true","fetch_graph":"https://pith.science/api/pith-number/QGLDK7V5GVVHAPKZSH4AOZI5NU/graph.json","fetch_events":"https://pith.science/api/pith-number/QGLDK7V5GVVHAPKZSH4AOZI5NU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGLDK7V5GVVHAPKZSH4AOZI5NU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGLDK7V5GVVHAPKZSH4AOZI5NU/action/storage_attestation","attest_author":"https://pith.science/pith/QGLDK7V5GVVHAPKZSH4AOZI5NU/action/author_attestation","sign_citation":"https://pith.science/pith/QGLDK7V5GVVHAPKZSH4AOZI5NU/action/citation_signature","submit_replication":"https://pith.science/pith/QGLDK7V5GVVHAPKZSH4AOZI5NU/action/replication_record"}},"created_at":"2026-05-18T00:33:01.221577+00:00","updated_at":"2026-05-18T00:33:01.221577+00:00"}