{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:JJNSNY6GBNFZ6LE74W6OILUW5S","short_pith_number":"pith:JJNSNY6G","canonical_record":{"source":{"id":"1601.02698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-01-12T00:09:18Z","cross_cats_sorted":[],"title_canon_sha256":"ac9680d1613898bd59dd22b60ec06dad48aefa598a6b2ae16fb8aef465186704","abstract_canon_sha256":"91bbc59416f673eece823a0fad9eb459c8177451f1c5133a5ee389a663554843"},"schema_version":"1.0"},"canonical_sha256":"4a5b26e3c60b4b9f2c9fe5bce42e96eca6b267d7c956049cb8fadf7cb1b4e0ef","source":{"kind":"arxiv","id":"1601.02698","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.02698","created_at":"2026-05-18T01:23:01Z"},{"alias_kind":"arxiv_version","alias_value":"1601.02698v1","created_at":"2026-05-18T01:23:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.02698","created_at":"2026-05-18T01:23:01Z"},{"alias_kind":"pith_short_12","alias_value":"JJNSNY6GBNFZ","created_at":"2026-05-18T12:30:25Z"},{"alias_kind":"pith_short_16","alias_value":"JJNSNY6GBNFZ6LE7","created_at":"2026-05-18T12:30:25Z"},{"alias_kind":"pith_short_8","alias_value":"JJNSNY6G","created_at":"2026-05-18T12:30:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:JJNSNY6GBNFZ6LE74W6OILUW5S","target":"record","payload":{"canonical_record":{"source":{"id":"1601.02698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-01-12T00:09:18Z","cross_cats_sorted":[],"title_canon_sha256":"ac9680d1613898bd59dd22b60ec06dad48aefa598a6b2ae16fb8aef465186704","abstract_canon_sha256":"91bbc59416f673eece823a0fad9eb459c8177451f1c5133a5ee389a663554843"},"schema_version":"1.0"},"canonical_sha256":"4a5b26e3c60b4b9f2c9fe5bce42e96eca6b267d7c956049cb8fadf7cb1b4e0ef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:23:01.675058Z","signature_b64":"gNVkGNU+W0GPnZJhQjuU9t3ChBALn4askl9U9SjCdMtXaZww9l8WxRsWMOWzOCc1yhaRkvlnMDrUewi4hzVJDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a5b26e3c60b4b9f2c9fe5bce42e96eca6b267d7c956049cb8fadf7cb1b4e0ef","last_reissued_at":"2026-05-18T01:23:01.674430Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:23:01.674430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1601.02698","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":"2026-05-18T01:23:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y35DalXIUJFqWD/zorbw+9HglLJXawOaw4DLWRofCBM981DDuEE5FX2hDVPngI57teLClbhM1eASnVd6zhHbDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T02:19:37.522085Z"},"content_sha256":"02d6693770f783e21547cc9bbc377cdd3c582a47a6073dab0755d811ecce4184","schema_version":"1.0","event_id":"sha256:02d6693770f783e21547cc9bbc377cdd3c582a47a6073dab0755d811ecce4184"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:JJNSNY6GBNFZ6LE74W6OILUW5S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Markov Chain Monte Carlo Sampling for Hierarchical Hidden Markov Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Christopher J. Paciorek, Daniel Turek, Perry de Valpine","submitted_at":"2016-01-12T00:09:18Z","abstract_excerpt":"Traditional Markov chain Monte Carlo (MCMC) sampling of hidden Markov models (HMMs) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance latent variables. When potentially many HMMs are embedded within a hierarchical model, this can result in prohibitively long MCMC runtimes. We study combinations of existing methods, which are shown to vastly improve computational efficiency for these hierarchical models while maintaining the modeling flexibility provided by embedded HMMs. The methods include di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.02698","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":"2026-05-18T01:23:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"un+mMF6Wox/eCgKemjZsb5zA9rqfQgiSoc1W8RLWjVoprov1F+Gf0QQycBl/XFNfhX3+Vbets/HTJDP4G3VbBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T02:19:37.522740Z"},"content_sha256":"ecda7e0704e2ea88f7024acfd57ffd857a8b593957f7d33880fc8a49add4009f","schema_version":"1.0","event_id":"sha256:ecda7e0704e2ea88f7024acfd57ffd857a8b593957f7d33880fc8a49add4009f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JJNSNY6GBNFZ6LE74W6OILUW5S/bundle.json","state_url":"https://pith.science/pith/JJNSNY6GBNFZ6LE74W6OILUW5S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JJNSNY6GBNFZ6LE74W6OILUW5S/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":"2026-06-07T02:19:37Z","links":{"resolver":"https://pith.science/pith/JJNSNY6GBNFZ6LE74W6OILUW5S","bundle":"https://pith.science/pith/JJNSNY6GBNFZ6LE74W6OILUW5S/bundle.json","state":"https://pith.science/pith/JJNSNY6GBNFZ6LE74W6OILUW5S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JJNSNY6GBNFZ6LE74W6OILUW5S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:JJNSNY6GBNFZ6LE74W6OILUW5S","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":"91bbc59416f673eece823a0fad9eb459c8177451f1c5133a5ee389a663554843","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-01-12T00:09:18Z","title_canon_sha256":"ac9680d1613898bd59dd22b60ec06dad48aefa598a6b2ae16fb8aef465186704"},"schema_version":"1.0","source":{"id":"1601.02698","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.02698","created_at":"2026-05-18T01:23:01Z"},{"alias_kind":"arxiv_version","alias_value":"1601.02698v1","created_at":"2026-05-18T01:23:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.02698","created_at":"2026-05-18T01:23:01Z"},{"alias_kind":"pith_short_12","alias_value":"JJNSNY6GBNFZ","created_at":"2026-05-18T12:30:25Z"},{"alias_kind":"pith_short_16","alias_value":"JJNSNY6GBNFZ6LE7","created_at":"2026-05-18T12:30:25Z"},{"alias_kind":"pith_short_8","alias_value":"JJNSNY6G","created_at":"2026-05-18T12:30:25Z"}],"graph_snapshots":[{"event_id":"sha256:ecda7e0704e2ea88f7024acfd57ffd857a8b593957f7d33880fc8a49add4009f","target":"graph","created_at":"2026-05-18T01:23:01Z","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":"Traditional Markov chain Monte Carlo (MCMC) sampling of hidden Markov models (HMMs) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance latent variables. When potentially many HMMs are embedded within a hierarchical model, this can result in prohibitively long MCMC runtimes. We study combinations of existing methods, which are shown to vastly improve computational efficiency for these hierarchical models while maintaining the modeling flexibility provided by embedded HMMs. The methods include di","authors_text":"Christopher J. Paciorek, Daniel Turek, Perry de Valpine","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-01-12T00:09:18Z","title":"Efficient Markov Chain Monte Carlo Sampling for Hierarchical Hidden Markov Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.02698","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":"sha256:02d6693770f783e21547cc9bbc377cdd3c582a47a6073dab0755d811ecce4184","target":"record","created_at":"2026-05-18T01:23:01Z","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":"91bbc59416f673eece823a0fad9eb459c8177451f1c5133a5ee389a663554843","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-01-12T00:09:18Z","title_canon_sha256":"ac9680d1613898bd59dd22b60ec06dad48aefa598a6b2ae16fb8aef465186704"},"schema_version":"1.0","source":{"id":"1601.02698","kind":"arxiv","version":1}},"canonical_sha256":"4a5b26e3c60b4b9f2c9fe5bce42e96eca6b267d7c956049cb8fadf7cb1b4e0ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4a5b26e3c60b4b9f2c9fe5bce42e96eca6b267d7c956049cb8fadf7cb1b4e0ef","first_computed_at":"2026-05-18T01:23:01.674430Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:23:01.674430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gNVkGNU+W0GPnZJhQjuU9t3ChBALn4askl9U9SjCdMtXaZww9l8WxRsWMOWzOCc1yhaRkvlnMDrUewi4hzVJDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:23:01.675058Z","signed_message":"canonical_sha256_bytes"},"source_id":"1601.02698","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:02d6693770f783e21547cc9bbc377cdd3c582a47a6073dab0755d811ecce4184","sha256:ecda7e0704e2ea88f7024acfd57ffd857a8b593957f7d33880fc8a49add4009f"],"state_sha256":"748b7bb48a5ca81508d3d205c3b7216b41074912f8dced462d8589d4c764f4de"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/R4HWCmtjs54aRLO127CQqtsiivIt2aaSG8iXcb/utSRGrHN6VoZjVCSB069CayUZ56oPrueef1e7Hj3/SqyDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T02:19:37.525392Z","bundle_sha256":"8b224bed95e693fee757c0dd4e11e32137251ee1721764fa08534d1b6c1ae44d"}}