{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:JBLA7EPGZUIZWRZUPXEUNXHIQG","short_pith_number":"pith:JBLA7EPG","canonical_record":{"source":{"id":"1904.08356","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-17T16:45:05Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"655c63e3c0ecdb841f748569be41fc12b75a06af7bd9cec6db0e54efb161359c","abstract_canon_sha256":"1d37a5120bf07f2877bfe5cb2f4e9b299a40dad788d56c1b6abe0046d4a4660a"},"schema_version":"1.0"},"canonical_sha256":"48560f91e6cd119b47347dc946dce8818c6769bcf73dd3418f9681e7ce7e4b5b","source":{"kind":"arxiv","id":"1904.08356","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.08356","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"arxiv_version","alias_value":"1904.08356v1","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.08356","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"pith_short_12","alias_value":"JBLA7EPGZUIZ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"JBLA7EPGZUIZWRZU","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"JBLA7EPG","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:JBLA7EPGZUIZWRZUPXEUNXHIQG","target":"record","payload":{"canonical_record":{"source":{"id":"1904.08356","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-17T16:45:05Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"655c63e3c0ecdb841f748569be41fc12b75a06af7bd9cec6db0e54efb161359c","abstract_canon_sha256":"1d37a5120bf07f2877bfe5cb2f4e9b299a40dad788d56c1b6abe0046d4a4660a"},"schema_version":"1.0"},"canonical_sha256":"48560f91e6cd119b47347dc946dce8818c6769bcf73dd3418f9681e7ce7e4b5b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:17.921807Z","signature_b64":"dNDhB0FHOMi11tP6ehulu4VoAp6zkAlIRSMUu4oZeDJ+XAnKzjkDMzJ/86v2fwboSVbfJTd9eoOx/8GQoKXKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48560f91e6cd119b47347dc946dce8818c6769bcf73dd3418f9681e7ce7e4b5b","last_reissued_at":"2026-05-17T23:48:17.921229Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:17.921229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.08356","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":"2026-05-17T23:48:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UH1UG3ItKZo8dYCbB6YHwCYMHsbrBdMfS2gGbFYaPmGppEpOeIm/NCQZV/8GCYnOfWRWaha6DDwr9cNRag7yDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T21:13:17.751018Z"},"content_sha256":"2e1a7955c57b0f9a54ecf3cb139febc35635c1ef2ed10db30ffa0638b0761904","schema_version":"1.0","event_id":"sha256:2e1a7955c57b0f9a54ecf3cb139febc35635c1ef2ed10db30ffa0638b0761904"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:JBLA7EPGZUIZWRZUPXEUNXHIQG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable Bayesian Inference for Population Markov Jump Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Iker Perez, Theodore Kypraios","submitted_at":"2019-04-17T16:45:05Z","abstract_excerpt":"Bayesian inference for Markov jump processes (MJPs) where available observations relate to either system states or jumps typically relies on data-augmentation Markov Chain Monte Carlo. State-of-the-art developments involve representing MJP paths with auxiliary candidate jump times that are later thinned. However, these algorithms are i) unfeasible in situations involving large or infinite capacity systems and ii) not amenable for all observation types. In this paper we establish and present a general data-augmentation framework for population MJPs based on uniformized representations of the un"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08356","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":"2026-05-17T23:48:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oOxk6fbQqJlRC2PaKSvZtjvsII6ujcPm/0OoaeVemUb4H4q6z4zNm+YVAWd2ot/kZhHmSVFUNX5T+nIKd3XRAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T21:13:17.751398Z"},"content_sha256":"2e69c9629b4e2f1e649a2c62bb5880f48516e4f10741c1c3007c4512d4aaf473","schema_version":"1.0","event_id":"sha256:2e69c9629b4e2f1e649a2c62bb5880f48516e4f10741c1c3007c4512d4aaf473"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JBLA7EPGZUIZWRZUPXEUNXHIQG/bundle.json","state_url":"https://pith.science/pith/JBLA7EPGZUIZWRZUPXEUNXHIQG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JBLA7EPGZUIZWRZUPXEUNXHIQG/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":"2026-06-02T21:13:17Z","links":{"resolver":"https://pith.science/pith/JBLA7EPGZUIZWRZUPXEUNXHIQG","bundle":"https://pith.science/pith/JBLA7EPGZUIZWRZUPXEUNXHIQG/bundle.json","state":"https://pith.science/pith/JBLA7EPGZUIZWRZUPXEUNXHIQG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JBLA7EPGZUIZWRZUPXEUNXHIQG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:JBLA7EPGZUIZWRZUPXEUNXHIQG","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":"1d37a5120bf07f2877bfe5cb2f4e9b299a40dad788d56c1b6abe0046d4a4660a","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-17T16:45:05Z","title_canon_sha256":"655c63e3c0ecdb841f748569be41fc12b75a06af7bd9cec6db0e54efb161359c"},"schema_version":"1.0","source":{"id":"1904.08356","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.08356","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"arxiv_version","alias_value":"1904.08356v1","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.08356","created_at":"2026-05-17T23:48:17Z"},{"alias_kind":"pith_short_12","alias_value":"JBLA7EPGZUIZ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"JBLA7EPGZUIZWRZU","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"JBLA7EPG","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:2e69c9629b4e2f1e649a2c62bb5880f48516e4f10741c1c3007c4512d4aaf473","target":"graph","created_at":"2026-05-17T23:48:17Z","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":"Bayesian inference for Markov jump processes (MJPs) where available observations relate to either system states or jumps typically relies on data-augmentation Markov Chain Monte Carlo. State-of-the-art developments involve representing MJP paths with auxiliary candidate jump times that are later thinned. However, these algorithms are i) unfeasible in situations involving large or infinite capacity systems and ii) not amenable for all observation types. In this paper we establish and present a general data-augmentation framework for population MJPs based on uniformized representations of the un","authors_text":"Iker Perez, Theodore Kypraios","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-17T16:45:05Z","title":"Scalable Bayesian Inference for Population Markov Jump Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08356","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":"sha256:2e1a7955c57b0f9a54ecf3cb139febc35635c1ef2ed10db30ffa0638b0761904","target":"record","created_at":"2026-05-17T23:48:17Z","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":"1d37a5120bf07f2877bfe5cb2f4e9b299a40dad788d56c1b6abe0046d4a4660a","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-17T16:45:05Z","title_canon_sha256":"655c63e3c0ecdb841f748569be41fc12b75a06af7bd9cec6db0e54efb161359c"},"schema_version":"1.0","source":{"id":"1904.08356","kind":"arxiv","version":1}},"canonical_sha256":"48560f91e6cd119b47347dc946dce8818c6769bcf73dd3418f9681e7ce7e4b5b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"48560f91e6cd119b47347dc946dce8818c6769bcf73dd3418f9681e7ce7e4b5b","first_computed_at":"2026-05-17T23:48:17.921229Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:17.921229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dNDhB0FHOMi11tP6ehulu4VoAp6zkAlIRSMUu4oZeDJ+XAnKzjkDMzJ/86v2fwboSVbfJTd9eoOx/8GQoKXKDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:17.921807Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.08356","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2e1a7955c57b0f9a54ecf3cb139febc35635c1ef2ed10db30ffa0638b0761904","sha256:2e69c9629b4e2f1e649a2c62bb5880f48516e4f10741c1c3007c4512d4aaf473"],"state_sha256":"e29e5900ca42aecdb84adae9e3d842fcb8cd43490af782fb3611d2750d805518"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bCKF+ljThRZvRQGPPwEfvLaSVPn9/+adea5lxnr+qrrKgxBLh+TyWN9DzHlBnK8eQztYbhhMKrBU2W+qxjMwCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T21:13:17.754321Z","bundle_sha256":"29fcfca934a9158367e8f26244bf79b5482260ac1231af64c58603dcb36dc102"}}