{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:JDQCORZQIRFVJBFAALT725FG55","short_pith_number":"pith:JDQCORZQ","canonical_record":{"source":{"id":"1905.10252","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-24T14:25:37Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"8bb02bb21c7becf32d39e055292b7a35a44ab07160e23e37eeb38daa43fa275b","abstract_canon_sha256":"390ee36199a8e9ee616a3aea7d7c18c23bc130d68a4e1017af2acc10af04052d"},"schema_version":"1.0"},"canonical_sha256":"48e0274730444b5484a002e7fd74a6ef443509732fe0bd93564c536c04814020","source":{"kind":"arxiv","id":"1905.10252","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10252","created_at":"2026-05-17T23:45:10Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10252v1","created_at":"2026-05-17T23:45:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10252","created_at":"2026-05-17T23:45:10Z"},{"alias_kind":"pith_short_12","alias_value":"JDQCORZQIRFV","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"JDQCORZQIRFVJBFA","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"JDQCORZQ","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:JDQCORZQIRFVJBFAALT725FG55","target":"record","payload":{"canonical_record":{"source":{"id":"1905.10252","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-24T14:25:37Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"8bb02bb21c7becf32d39e055292b7a35a44ab07160e23e37eeb38daa43fa275b","abstract_canon_sha256":"390ee36199a8e9ee616a3aea7d7c18c23bc130d68a4e1017af2acc10af04052d"},"schema_version":"1.0"},"canonical_sha256":"48e0274730444b5484a002e7fd74a6ef443509732fe0bd93564c536c04814020","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:10.816248Z","signature_b64":"j48NjAaRUSxCMs4/BSGEWXPlpAMKzpq8PliDb7R0/QeTh6Bj3LWuF/rxAPLbjcYSQ58J1NK6UdXQDDEZBz67AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48e0274730444b5484a002e7fd74a6ef443509732fe0bd93564c536c04814020","last_reissued_at":"2026-05-17T23:45:10.815210Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:10.815210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.10252","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":"2026-05-17T23:45:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z/fmTjuois3t/4JI8rWOe1y1ugzNdTsnePwHN+nQPu/oFC4pSm01KTdqT6EvbsIa2nVdgdftGtplfC/t7EMiDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T10:40:40.277812Z"},"content_sha256":"67d3cedb29865cbb34da3138d0b7aafd03a6f2ecfd9bb8689b5a088f1ec67f63","schema_version":"1.0","event_id":"sha256:67d3cedb29865cbb34da3138d0b7aafd03a6f2ecfd9bb8689b5a088f1ec67f63"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:JDQCORZQIRFVJBFAALT725FG55","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"stat.CO","authors_text":"Alessandro Varsi, Jeyarajan Thiyagalingam, Lykourgos Kekempanos, Simon Maskell","submitted_at":"2019-05-24T14:25:37Z","abstract_excerpt":"Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms which are primarily used in Bayesian statistics to sample from a target distribution when direct sampling is challenging. Single instances of MCMC methods are widely considered hard to parallelise in a problem-agnostic fashion and hence, unsuitable to meet both constraints of high accuracy and high throughput. Sequential Monte Carlo (SMC) Samplers can address the same problem, but are parallelisable: they share with Particle Filters the same key tasks and bottleneck. Although a rich literature already exists on MCMC met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10252","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":"2026-05-17T23:45:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DYJ28EY4QlRYvbPtPgwrt0NCrx7q2FLe06k7WTLSpkt17Blv1Loym4K6j0A0oiuRAshEU2hDm3BYwi55j+l/CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T10:40:40.278159Z"},"content_sha256":"c24150b62dd54deba33c227a99034cd77413c57d65af9509825b4ed8fffe2536","schema_version":"1.0","event_id":"sha256:c24150b62dd54deba33c227a99034cd77413c57d65af9509825b4ed8fffe2536"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JDQCORZQIRFVJBFAALT725FG55/bundle.json","state_url":"https://pith.science/pith/JDQCORZQIRFVJBFAALT725FG55/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JDQCORZQIRFVJBFAALT725FG55/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":"2026-06-26T10:40:40Z","links":{"resolver":"https://pith.science/pith/JDQCORZQIRFVJBFAALT725FG55","bundle":"https://pith.science/pith/JDQCORZQIRFVJBFAALT725FG55/bundle.json","state":"https://pith.science/pith/JDQCORZQIRFVJBFAALT725FG55/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JDQCORZQIRFVJBFAALT725FG55/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:JDQCORZQIRFVJBFAALT725FG55","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":"390ee36199a8e9ee616a3aea7d7c18c23bc130d68a4e1017af2acc10af04052d","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-24T14:25:37Z","title_canon_sha256":"8bb02bb21c7becf32d39e055292b7a35a44ab07160e23e37eeb38daa43fa275b"},"schema_version":"1.0","source":{"id":"1905.10252","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10252","created_at":"2026-05-17T23:45:10Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10252v1","created_at":"2026-05-17T23:45:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10252","created_at":"2026-05-17T23:45:10Z"},{"alias_kind":"pith_short_12","alias_value":"JDQCORZQIRFV","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"JDQCORZQIRFVJBFA","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"JDQCORZQ","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:c24150b62dd54deba33c227a99034cd77413c57d65af9509825b4ed8fffe2536","target":"graph","created_at":"2026-05-17T23:45:10Z","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":"Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms which are primarily used in Bayesian statistics to sample from a target distribution when direct sampling is challenging. Single instances of MCMC methods are widely considered hard to parallelise in a problem-agnostic fashion and hence, unsuitable to meet both constraints of high accuracy and high throughput. Sequential Monte Carlo (SMC) Samplers can address the same problem, but are parallelisable: they share with Particle Filters the same key tasks and bottleneck. Although a rich literature already exists on MCMC met","authors_text":"Alessandro Varsi, Jeyarajan Thiyagalingam, Lykourgos Kekempanos, Simon Maskell","cross_cats":["cs.DC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-24T14:25:37Z","title":"A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10252","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":"sha256:67d3cedb29865cbb34da3138d0b7aafd03a6f2ecfd9bb8689b5a088f1ec67f63","target":"record","created_at":"2026-05-17T23:45:10Z","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":"390ee36199a8e9ee616a3aea7d7c18c23bc130d68a4e1017af2acc10af04052d","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-24T14:25:37Z","title_canon_sha256":"8bb02bb21c7becf32d39e055292b7a35a44ab07160e23e37eeb38daa43fa275b"},"schema_version":"1.0","source":{"id":"1905.10252","kind":"arxiv","version":1}},"canonical_sha256":"48e0274730444b5484a002e7fd74a6ef443509732fe0bd93564c536c04814020","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"48e0274730444b5484a002e7fd74a6ef443509732fe0bd93564c536c04814020","first_computed_at":"2026-05-17T23:45:10.815210Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:45:10.815210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j48NjAaRUSxCMs4/BSGEWXPlpAMKzpq8PliDb7R0/QeTh6Bj3LWuF/rxAPLbjcYSQ58J1NK6UdXQDDEZBz67AQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:45:10.816248Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.10252","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67d3cedb29865cbb34da3138d0b7aafd03a6f2ecfd9bb8689b5a088f1ec67f63","sha256:c24150b62dd54deba33c227a99034cd77413c57d65af9509825b4ed8fffe2536"],"state_sha256":"02cf352a5567fdca933bf9a4b715b03e278ac75944a99bd4c26cb368c36f1b1c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oova/ByqyxPCYSGXHq3OTXlbA+Q5z73qgh16kMPSyR63cD9JXplC/QRZ+A+UIDuV5gZ5s+NnQsg8z4v4tPsVBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T10:40:40.279985Z","bundle_sha256":"94f498b9f210fe5740fbcf42d18b5a3ba9d85a416e5f1268da79ad91d37a5ba3"}}