{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:P6BC7ED7XSBD3KDN5EWVJZUER2","short_pith_number":"pith:P6BC7ED7","canonical_record":{"source":{"id":"1401.0604","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-01-03T08:17:51Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"337234e263d7b6b9577bf73394e3bc68da191b322a77f35fe6331c1532b79b56","abstract_canon_sha256":"0a56d35daa5c54d7f85763abc0bf35defe6d6f773c87d455b3f48f3ae28c1e7a"},"schema_version":"1.0"},"canonical_sha256":"7f822f907fbc823da86de92d54e6848ea77215313227024bbc0964a5992c8102","source":{"kind":"arxiv","id":"1401.0604","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1401.0604","created_at":"2026-05-18T02:42:46Z"},{"alias_kind":"arxiv_version","alias_value":"1401.0604v1","created_at":"2026-05-18T02:42:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.0604","created_at":"2026-05-18T02:42:46Z"},{"alias_kind":"pith_short_12","alias_value":"P6BC7ED7XSBD","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"P6BC7ED7XSBD3KDN","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"P6BC7ED7","created_at":"2026-05-18T12:28:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:P6BC7ED7XSBD3KDN5EWVJZUER2","target":"record","payload":{"canonical_record":{"source":{"id":"1401.0604","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-01-03T08:17:51Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"337234e263d7b6b9577bf73394e3bc68da191b322a77f35fe6331c1532b79b56","abstract_canon_sha256":"0a56d35daa5c54d7f85763abc0bf35defe6d6f773c87d455b3f48f3ae28c1e7a"},"schema_version":"1.0"},"canonical_sha256":"7f822f907fbc823da86de92d54e6848ea77215313227024bbc0964a5992c8102","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:42:46.499290Z","signature_b64":"PyT/mcLoqpnITXOUhR3pUNaJtRKGtUPL1zKiCP3wyGxy+dwQwoeBDY6UxGq0Q2zQBRJy7Y8waiaIFshfE/IJCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f822f907fbc823da86de92d54e6848ea77215313227024bbc0964a5992c8102","last_reissued_at":"2026-05-18T02:42:46.498831Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:42:46.498831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1401.0604","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:42:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bpn5VhrDT/8d/d/17R/vlOY6NykrZe6Kgszm6IfQws8/A+NMJLwpd/edke8/tdyiI0uRSqKdhq4c9O7HbQ6sCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T06:52:45.385732Z"},"content_sha256":"70057e34a4bada7ed59f754ea756c95309b6679839d4ec234d82a7e907c11bb9","schema_version":"1.0","event_id":"sha256:70057e34a4bada7ed59f754ea756c95309b6679839d4ec234d82a7e907c11bb9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:P6BC7ED7XSBD3KDN5EWVJZUER2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Particle Gibbs with Ancestor Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Fredrik Lindsten, Michael I. Jordan, Thomas B. Sch\\\"on","submitted_at":"2014-01-03T08:17:51Z","abstract_excerpt":"Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). We present a novel PMCMC algorithm that we refer to as particle Gibbs with ancestor sampling (PGAS). PGAS provides the data analyst with an off-the-shelf class of Markov kernels that can be used to simulate the typically high-dimensional and highly autocorrelated state trajectory in a state-space model. The ancestor sampling procedure enables fast mixing of the PGAS kernel even when using seem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.0604","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:42:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j4U+jykY4P8pNzZ0QDhXpHsBDHUVrYbPvWC11RP1bZVBsJG3MYmyOcnNhzStcnoTw9wNKF4yjSnkgDZrnSoVDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T06:52:45.386066Z"},"content_sha256":"2ddeb5839f2e0ff361b110526e34e2486c273882131318b18eea017301643250","schema_version":"1.0","event_id":"sha256:2ddeb5839f2e0ff361b110526e34e2486c273882131318b18eea017301643250"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P6BC7ED7XSBD3KDN5EWVJZUER2/bundle.json","state_url":"https://pith.science/pith/P6BC7ED7XSBD3KDN5EWVJZUER2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P6BC7ED7XSBD3KDN5EWVJZUER2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-03T06:52:45Z","links":{"resolver":"https://pith.science/pith/P6BC7ED7XSBD3KDN5EWVJZUER2","bundle":"https://pith.science/pith/P6BC7ED7XSBD3KDN5EWVJZUER2/bundle.json","state":"https://pith.science/pith/P6BC7ED7XSBD3KDN5EWVJZUER2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P6BC7ED7XSBD3KDN5EWVJZUER2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:P6BC7ED7XSBD3KDN5EWVJZUER2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0a56d35daa5c54d7f85763abc0bf35defe6d6f773c87d455b3f48f3ae28c1e7a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-01-03T08:17:51Z","title_canon_sha256":"337234e263d7b6b9577bf73394e3bc68da191b322a77f35fe6331c1532b79b56"},"schema_version":"1.0","source":{"id":"1401.0604","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1401.0604","created_at":"2026-05-18T02:42:46Z"},{"alias_kind":"arxiv_version","alias_value":"1401.0604v1","created_at":"2026-05-18T02:42:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.0604","created_at":"2026-05-18T02:42:46Z"},{"alias_kind":"pith_short_12","alias_value":"P6BC7ED7XSBD","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"P6BC7ED7XSBD3KDN","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"P6BC7ED7","created_at":"2026-05-18T12:28:43Z"}],"graph_snapshots":[{"event_id":"sha256:2ddeb5839f2e0ff361b110526e34e2486c273882131318b18eea017301643250","target":"graph","created_at":"2026-05-18T02:42:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). We present a novel PMCMC algorithm that we refer to as particle Gibbs with ancestor sampling (PGAS). PGAS provides the data analyst with an off-the-shelf class of Markov kernels that can be used to simulate the typically high-dimensional and highly autocorrelated state trajectory in a state-space model. The ancestor sampling procedure enables fast mixing of the PGAS kernel even when using seem","authors_text":"Fredrik Lindsten, Michael I. Jordan, Thomas B. Sch\\\"on","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-01-03T08:17:51Z","title":"Particle Gibbs with Ancestor Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.0604","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:70057e34a4bada7ed59f754ea756c95309b6679839d4ec234d82a7e907c11bb9","target":"record","created_at":"2026-05-18T02:42:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0a56d35daa5c54d7f85763abc0bf35defe6d6f773c87d455b3f48f3ae28c1e7a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-01-03T08:17:51Z","title_canon_sha256":"337234e263d7b6b9577bf73394e3bc68da191b322a77f35fe6331c1532b79b56"},"schema_version":"1.0","source":{"id":"1401.0604","kind":"arxiv","version":1}},"canonical_sha256":"7f822f907fbc823da86de92d54e6848ea77215313227024bbc0964a5992c8102","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f822f907fbc823da86de92d54e6848ea77215313227024bbc0964a5992c8102","first_computed_at":"2026-05-18T02:42:46.498831Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:42:46.498831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PyT/mcLoqpnITXOUhR3pUNaJtRKGtUPL1zKiCP3wyGxy+dwQwoeBDY6UxGq0Q2zQBRJy7Y8waiaIFshfE/IJCg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:42:46.499290Z","signed_message":"canonical_sha256_bytes"},"source_id":"1401.0604","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70057e34a4bada7ed59f754ea756c95309b6679839d4ec234d82a7e907c11bb9","sha256:2ddeb5839f2e0ff361b110526e34e2486c273882131318b18eea017301643250"],"state_sha256":"7c2852fc379cbf7dacc73fffe1469516c58536d48fe876a9305f71712aecc759"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T1FBK8/fN1MYTwn+G2UNRW2nhOl7J/tSyuzU7eWz0p7u4ys0QUAf2TRdfZ0pVMJJbHTZEXWlJkFerz9+uHPJBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T06:52:45.387949Z","bundle_sha256":"765a5848042f56d524c26b384142a34c93c1a5574f66852b81fc267b01b4fee9"}}