{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:6VQQLPNOLDQDRTOVFOVTEHBPPY","short_pith_number":"pith:6VQQLPNO","schema_version":"1.0","canonical_sha256":"f56105bdae58e038cdd52bab321c2f7e27848611eb213c68a15c10c5c1f95013","source":{"kind":"arxiv","id":"1801.08244","version":2},"attestation_state":"computed","paper":{"title":"Importance sampling for partially observed temporal epidemic models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.PE","authors_text":"Andrew J. Black","submitted_at":"2018-01-24T23:49:21Z","abstract_excerpt":"We present an importance sampling algorithm that can produce realisations of Markovian epidemic models that exactly match observations, taken to be the number of a single event type over a period of time. The importance sampling can be used to construct an efficient particle filter that targets the states of a system and hence estimate the likelihood to perform Bayesian parameter inference. When used in a particle marginal Metropolis Hastings scheme, the importance sampling provides a large speed-up in terms of the effective sample size per unit of computational time, compared to simple bootst"},"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":"1801.08244","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.PE","submitted_at":"2018-01-24T23:49:21Z","cross_cats_sorted":[],"title_canon_sha256":"b1054361b1912cf700b2832ef537aa9b5eef10407e6159d7ef5c3e0509bcbd27","abstract_canon_sha256":"3e4147c89e1c346c19d0041fe96222bf2fa9c2dfa252e4e1437dcf31e4223981"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:03.104974Z","signature_b64":"qkeznvy2jCrFQ4UVVnpjLUuA8jqhAyPT3nA1AT/EEjolhoLdhnqYtGSL8pqB5T2+VHquYueuMUH/XlmB5zssAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f56105bdae58e038cdd52bab321c2f7e27848611eb213c68a15c10c5c1f95013","last_reissued_at":"2026-05-18T00:08:03.104372Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:03.104372Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Importance sampling for partially observed temporal epidemic models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.PE","authors_text":"Andrew J. Black","submitted_at":"2018-01-24T23:49:21Z","abstract_excerpt":"We present an importance sampling algorithm that can produce realisations of Markovian epidemic models that exactly match observations, taken to be the number of a single event type over a period of time. The importance sampling can be used to construct an efficient particle filter that targets the states of a system and hence estimate the likelihood to perform Bayesian parameter inference. When used in a particle marginal Metropolis Hastings scheme, the importance sampling provides a large speed-up in terms of the effective sample size per unit of computational time, compared to simple bootst"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.08244","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":"1801.08244","created_at":"2026-05-18T00:08:03.104472+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.08244v2","created_at":"2026-05-18T00:08:03.104472+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.08244","created_at":"2026-05-18T00:08:03.104472+00:00"},{"alias_kind":"pith_short_12","alias_value":"6VQQLPNOLDQD","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"6VQQLPNOLDQDRTOV","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"6VQQLPNO","created_at":"2026-05-18T12:32:11.075285+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/6VQQLPNOLDQDRTOVFOVTEHBPPY","json":"https://pith.science/pith/6VQQLPNOLDQDRTOVFOVTEHBPPY.json","graph_json":"https://pith.science/api/pith-number/6VQQLPNOLDQDRTOVFOVTEHBPPY/graph.json","events_json":"https://pith.science/api/pith-number/6VQQLPNOLDQDRTOVFOVTEHBPPY/events.json","paper":"https://pith.science/paper/6VQQLPNO"},"agent_actions":{"view_html":"https://pith.science/pith/6VQQLPNOLDQDRTOVFOVTEHBPPY","download_json":"https://pith.science/pith/6VQQLPNOLDQDRTOVFOVTEHBPPY.json","view_paper":"https://pith.science/paper/6VQQLPNO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.08244&json=true","fetch_graph":"https://pith.science/api/pith-number/6VQQLPNOLDQDRTOVFOVTEHBPPY/graph.json","fetch_events":"https://pith.science/api/pith-number/6VQQLPNOLDQDRTOVFOVTEHBPPY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6VQQLPNOLDQDRTOVFOVTEHBPPY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6VQQLPNOLDQDRTOVFOVTEHBPPY/action/storage_attestation","attest_author":"https://pith.science/pith/6VQQLPNOLDQDRTOVFOVTEHBPPY/action/author_attestation","sign_citation":"https://pith.science/pith/6VQQLPNOLDQDRTOVFOVTEHBPPY/action/citation_signature","submit_replication":"https://pith.science/pith/6VQQLPNOLDQDRTOVFOVTEHBPPY/action/replication_record"}},"created_at":"2026-05-18T00:08:03.104472+00:00","updated_at":"2026-05-18T00:08:03.104472+00:00"}