{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:F6TREH6QW4ST6OTWFHGG36I57N","short_pith_number":"pith:F6TREH6Q","canonical_record":{"source":{"id":"1412.5565","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-12-17T20:33:58Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"d72250c9c17f147c707d5870ace0d3b2316bb5b7b99feea1b8772670a61c3f08","abstract_canon_sha256":"83b02fae32e2ec8c3680d71de7a9cab7d86ecc193fc7fef560922ee80995ab4e"},"schema_version":"1.0"},"canonical_sha256":"2fa7121fd0b7253f3a7629cc6df91dfb7b8ac0aabd246646afbedfae7fa04a09","source":{"kind":"arxiv","id":"1412.5565","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.5565","created_at":"2026-05-18T01:35:20Z"},{"alias_kind":"arxiv_version","alias_value":"1412.5565v2","created_at":"2026-05-18T01:35:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.5565","created_at":"2026-05-18T01:35:20Z"},{"alias_kind":"pith_short_12","alias_value":"F6TREH6QW4ST","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"F6TREH6QW4ST6OTW","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"F6TREH6Q","created_at":"2026-05-18T12:28:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:F6TREH6QW4ST6OTWFHGG36I57N","target":"record","payload":{"canonical_record":{"source":{"id":"1412.5565","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-12-17T20:33:58Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"d72250c9c17f147c707d5870ace0d3b2316bb5b7b99feea1b8772670a61c3f08","abstract_canon_sha256":"83b02fae32e2ec8c3680d71de7a9cab7d86ecc193fc7fef560922ee80995ab4e"},"schema_version":"1.0"},"canonical_sha256":"2fa7121fd0b7253f3a7629cc6df91dfb7b8ac0aabd246646afbedfae7fa04a09","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:35:20.545144Z","signature_b64":"m/XrNiXx2Vjx6x/yC0AgAtcAkyEQYZ3bdiLGRKoT5Qho2lU67TFI0DDYR9qw+3+H2aLZv9KY18UoIQhpnPF0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fa7121fd0b7253f3a7629cc6df91dfb7b8ac0aabd246646afbedfae7fa04a09","last_reissued_at":"2026-05-18T01:35:20.544493Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:35:20.544493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1412.5565","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:35:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z+4/SZt0F2OJ9asMo2bEpfponvooUmd/gxH+MGr5XdH49xTpVVsNzUXLJoBvydA8KZUqoG7lzvd3Jv626PgdDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T21:24:35.559096Z"},"content_sha256":"85e6b65c3b091ae42693fb011c85292a7c5cf2d4c326d63c082ec13ef7822575","schema_version":"1.0","event_id":"sha256:85e6b65c3b091ae42693fb011c85292a7c5cf2d4c326d63c082ec13ef7822575"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:F6TREH6QW4ST6OTWFHGG36I57N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian detection of abnormal segments in multiple time series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.AP","authors_text":"Lawrence Bardwell, Paul Fearnhead","submitted_at":"2014-12-17T20:33:58Z","abstract_excerpt":"We present a novel Bayesian approach to analysing multiple time-series with the aim of detecting abnormal regions. These are regions where the properties of the data change from some normal or baseline behaviour. We allow for the possibility that such changes will only be present in a, potentially small, subset of the time-series. We develop a general model for this problem, and show how it is possible to accurately and efficiently perform Bayesian inference, based upon recursions that enable independent sampling from the posterior distribution. A motivating application for this problem comes "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.5565","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:35:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nqhuu6Ba7nlUdat55Nftyu2eQluzJXoAHfSdMcjNU7Opz+yWTi8iak0WGQdXC2Hb8IIZ9q8zS2b5Apc5peCXCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T21:24:35.559460Z"},"content_sha256":"4c53a07980402a321566c2d0e6817701f0152e065c467b2e8b1ef1125b32e1c2","schema_version":"1.0","event_id":"sha256:4c53a07980402a321566c2d0e6817701f0152e065c467b2e8b1ef1125b32e1c2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F6TREH6QW4ST6OTWFHGG36I57N/bundle.json","state_url":"https://pith.science/pith/F6TREH6QW4ST6OTWFHGG36I57N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F6TREH6QW4ST6OTWFHGG36I57N/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":"2026-06-29T21:24:35Z","links":{"resolver":"https://pith.science/pith/F6TREH6QW4ST6OTWFHGG36I57N","bundle":"https://pith.science/pith/F6TREH6QW4ST6OTWFHGG36I57N/bundle.json","state":"https://pith.science/pith/F6TREH6QW4ST6OTWFHGG36I57N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F6TREH6QW4ST6OTWFHGG36I57N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:F6TREH6QW4ST6OTWFHGG36I57N","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":"83b02fae32e2ec8c3680d71de7a9cab7d86ecc193fc7fef560922ee80995ab4e","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-12-17T20:33:58Z","title_canon_sha256":"d72250c9c17f147c707d5870ace0d3b2316bb5b7b99feea1b8772670a61c3f08"},"schema_version":"1.0","source":{"id":"1412.5565","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.5565","created_at":"2026-05-18T01:35:20Z"},{"alias_kind":"arxiv_version","alias_value":"1412.5565v2","created_at":"2026-05-18T01:35:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.5565","created_at":"2026-05-18T01:35:20Z"},{"alias_kind":"pith_short_12","alias_value":"F6TREH6QW4ST","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"F6TREH6QW4ST6OTW","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"F6TREH6Q","created_at":"2026-05-18T12:28:28Z"}],"graph_snapshots":[{"event_id":"sha256:4c53a07980402a321566c2d0e6817701f0152e065c467b2e8b1ef1125b32e1c2","target":"graph","created_at":"2026-05-18T01:35:20Z","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":"We present a novel Bayesian approach to analysing multiple time-series with the aim of detecting abnormal regions. These are regions where the properties of the data change from some normal or baseline behaviour. We allow for the possibility that such changes will only be present in a, potentially small, subset of the time-series. We develop a general model for this problem, and show how it is possible to accurately and efficiently perform Bayesian inference, based upon recursions that enable independent sampling from the posterior distribution. A motivating application for this problem comes ","authors_text":"Lawrence Bardwell, Paul Fearnhead","cross_cats":["stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-12-17T20:33:58Z","title":"Bayesian detection of abnormal segments in multiple time series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.5565","kind":"arxiv","version":2},"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":"sha256:85e6b65c3b091ae42693fb011c85292a7c5cf2d4c326d63c082ec13ef7822575","target":"record","created_at":"2026-05-18T01:35:20Z","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":"83b02fae32e2ec8c3680d71de7a9cab7d86ecc193fc7fef560922ee80995ab4e","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-12-17T20:33:58Z","title_canon_sha256":"d72250c9c17f147c707d5870ace0d3b2316bb5b7b99feea1b8772670a61c3f08"},"schema_version":"1.0","source":{"id":"1412.5565","kind":"arxiv","version":2}},"canonical_sha256":"2fa7121fd0b7253f3a7629cc6df91dfb7b8ac0aabd246646afbedfae7fa04a09","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2fa7121fd0b7253f3a7629cc6df91dfb7b8ac0aabd246646afbedfae7fa04a09","first_computed_at":"2026-05-18T01:35:20.544493Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:35:20.544493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"m/XrNiXx2Vjx6x/yC0AgAtcAkyEQYZ3bdiLGRKoT5Qho2lU67TFI0DDYR9qw+3+H2aLZv9KY18UoIQhpnPF0Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:35:20.545144Z","signed_message":"canonical_sha256_bytes"},"source_id":"1412.5565","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85e6b65c3b091ae42693fb011c85292a7c5cf2d4c326d63c082ec13ef7822575","sha256:4c53a07980402a321566c2d0e6817701f0152e065c467b2e8b1ef1125b32e1c2"],"state_sha256":"3bdf7b1bffb1c45a61fc2fee150b67165cfea0b2a6aafd4a0f84e034b6fc8f0b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z78hjPmPH928KEKCBBru1pX/fqZ7Su6/8UqeCdC5v1KnzohR5OL7dJ0KUNlgvL03LgIzzIPR9MBLopq0iAO5AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T21:24:35.562034Z","bundle_sha256":"3d22b108c581d883d121ec9dfb432678972756b26fce79efed3b2de1cb49b579"}}