{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:J3BUV5BO43LNAJXA2PLKTIATLE","short_pith_number":"pith:J3BUV5BO","canonical_record":{"source":{"id":"1408.4660","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-08-20T14:03:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"907ac07177ad6425093fb090b65ed02922dc7ee96eaf5b7d6bca3a595c1ebe2e","abstract_canon_sha256":"449147bd5e05062f19a42b7f370e88a6f17c9f040699200cb284264886554055"},"schema_version":"1.0"},"canonical_sha256":"4ec34af42ee6d6d026e0d3d6a9a0135918033897e2709fc27214c4ee7e2eb35b","source":{"kind":"arxiv","id":"1408.4660","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1408.4660","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"arxiv_version","alias_value":"1408.4660v2","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.4660","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"pith_short_12","alias_value":"J3BUV5BO43LN","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_16","alias_value":"J3BUV5BO43LNAJXA","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_8","alias_value":"J3BUV5BO","created_at":"2026-05-18T12:28:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:J3BUV5BO43LNAJXA2PLKTIATLE","target":"record","payload":{"canonical_record":{"source":{"id":"1408.4660","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-08-20T14:03:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"907ac07177ad6425093fb090b65ed02922dc7ee96eaf5b7d6bca3a595c1ebe2e","abstract_canon_sha256":"449147bd5e05062f19a42b7f370e88a6f17c9f040699200cb284264886554055"},"schema_version":"1.0"},"canonical_sha256":"4ec34af42ee6d6d026e0d3d6a9a0135918033897e2709fc27214c4ee7e2eb35b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:39.439667Z","signature_b64":"e8jSR3IrJm2X5jnV42KCKul2XcwBSaznAXuFrR1C+Q+WBAZzBeB/Xyws4vKSvR+DrdiiuGSDN1WNfM10qNIxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ec34af42ee6d6d026e0d3d6a9a0135918033897e2709fc27214c4ee7e2eb35b","last_reissued_at":"2026-05-18T02:44:39.439125Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:39.439125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1408.4660","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":"2026-05-18T02:44:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wavk+00f4Dbthxp6ZJW2Rph16EGIlm+N8NToq0MjDpsK8N6z1EG4mcE08THC4MFbI8AvILB1R3dXLloEezOrDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T21:10:06.956880Z"},"content_sha256":"bfe080d3d9cc4b1a83dbb58b0b1385a776633f1062ceb777132801cf967cb037","schema_version":"1.0","event_id":"sha256:bfe080d3d9cc4b1a83dbb58b0b1385a776633f1062ceb777132801cf967cb037"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:J3BUV5BO43LNAJXA2PLKTIATLE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Joint Hierarchical Gaussian Process Model with Application to Forecast in Medical Monitoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"John P. Clancy, Leo L. Duan, Rhonda D. Szczesniak","submitted_at":"2014-08-20T14:03:40Z","abstract_excerpt":"A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint modeling of continuous measurements and survival events. The baseline hazard, covariate and joint effects are conveniently modeled in this hierarchical structure. The estimation and inference are implemented in fully Bayesian framework using the objective and shrinkage priors. In si"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.4660","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":"2026-05-18T02:44:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nCby1p1A3UYmHm7xqSNKFpgLNZKF8sfbaYRcMVvMOaIQSzDm5YzeLuY5ee28PT7kPetxD3xK1xFbTIKoK6DkBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T21:10:06.957679Z"},"content_sha256":"d78ea28de97ce7bdc9c24a57dc70098b92ca6ba93ea40f7e3ae8594822b52fa1","schema_version":"1.0","event_id":"sha256:d78ea28de97ce7bdc9c24a57dc70098b92ca6ba93ea40f7e3ae8594822b52fa1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J3BUV5BO43LNAJXA2PLKTIATLE/bundle.json","state_url":"https://pith.science/pith/J3BUV5BO43LNAJXA2PLKTIATLE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J3BUV5BO43LNAJXA2PLKTIATLE/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":"2026-06-11T21:10:06Z","links":{"resolver":"https://pith.science/pith/J3BUV5BO43LNAJXA2PLKTIATLE","bundle":"https://pith.science/pith/J3BUV5BO43LNAJXA2PLKTIATLE/bundle.json","state":"https://pith.science/pith/J3BUV5BO43LNAJXA2PLKTIATLE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J3BUV5BO43LNAJXA2PLKTIATLE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:J3BUV5BO43LNAJXA2PLKTIATLE","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":"449147bd5e05062f19a42b7f370e88a6f17c9f040699200cb284264886554055","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-08-20T14:03:40Z","title_canon_sha256":"907ac07177ad6425093fb090b65ed02922dc7ee96eaf5b7d6bca3a595c1ebe2e"},"schema_version":"1.0","source":{"id":"1408.4660","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1408.4660","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"arxiv_version","alias_value":"1408.4660v2","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.4660","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"pith_short_12","alias_value":"J3BUV5BO43LN","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_16","alias_value":"J3BUV5BO43LNAJXA","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_8","alias_value":"J3BUV5BO","created_at":"2026-05-18T12:28:33Z"}],"graph_snapshots":[{"event_id":"sha256:d78ea28de97ce7bdc9c24a57dc70098b92ca6ba93ea40f7e3ae8594822b52fa1","target":"graph","created_at":"2026-05-18T02:44:39Z","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":"A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint modeling of continuous measurements and survival events. The baseline hazard, covariate and joint effects are conveniently modeled in this hierarchical structure. The estimation and inference are implemented in fully Bayesian framework using the objective and shrinkage priors. In si","authors_text":"John P. Clancy, Leo L. Duan, Rhonda D. Szczesniak","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-08-20T14:03:40Z","title":"Joint Hierarchical Gaussian Process Model with Application to Forecast in Medical Monitoring"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.4660","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:bfe080d3d9cc4b1a83dbb58b0b1385a776633f1062ceb777132801cf967cb037","target":"record","created_at":"2026-05-18T02:44:39Z","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":"449147bd5e05062f19a42b7f370e88a6f17c9f040699200cb284264886554055","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-08-20T14:03:40Z","title_canon_sha256":"907ac07177ad6425093fb090b65ed02922dc7ee96eaf5b7d6bca3a595c1ebe2e"},"schema_version":"1.0","source":{"id":"1408.4660","kind":"arxiv","version":2}},"canonical_sha256":"4ec34af42ee6d6d026e0d3d6a9a0135918033897e2709fc27214c4ee7e2eb35b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ec34af42ee6d6d026e0d3d6a9a0135918033897e2709fc27214c4ee7e2eb35b","first_computed_at":"2026-05-18T02:44:39.439125Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:39.439125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e8jSR3IrJm2X5jnV42KCKul2XcwBSaznAXuFrR1C+Q+WBAZzBeB/Xyws4vKSvR+DrdiiuGSDN1WNfM10qNIxBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:39.439667Z","signed_message":"canonical_sha256_bytes"},"source_id":"1408.4660","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bfe080d3d9cc4b1a83dbb58b0b1385a776633f1062ceb777132801cf967cb037","sha256:d78ea28de97ce7bdc9c24a57dc70098b92ca6ba93ea40f7e3ae8594822b52fa1"],"state_sha256":"27a30b4b46a1f51552021227b8cc06b9e6d68112380e9e3206249edf30e96039"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WXMTTCv7QR0xh6q/0zN9/EEknhMi94o/OhEeDfzr9fBUPjAiOHnnU9fxTOGon/EjhkIW/Wx0qExfxxQb8D3qBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T21:10:06.961705Z","bundle_sha256":"ff7b03732a8a3192f530795c1c8b5c3aa3844af3859e17f6ce5741ff6f0a1c36"}}