{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:SHGIJJVN7FWQKJUJQ32R4S7HTN","short_pith_number":"pith:SHGIJJVN","schema_version":"1.0","canonical_sha256":"91cc84a6adf96d05268986f51e4be79b444c1c11f75ce9df297fdc75ce4b0f85","source":{"kind":"arxiv","id":"1007.4532","version":2},"attestation_state":"computed","paper":{"title":"A decision-theoretic approach for segmental classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Christopher C. Holmes, Christopher Yau","submitted_at":"2010-07-26T19:03:11Z","abstract_excerpt":"This paper is concerned with statistical methods for the segmental classification of linear sequence data where the task is to segment and classify the data according to an underlying hidden discrete state sequence. Such analysis is commonplace in the empirical sciences including genomics, finance and speech processing. In particular, we are interested in answering the following question: given data $y$ and a statistical model $\\pi(x,y)$ of the hidden states $x$, what should we report as the prediction $\\hat{x}$ under the posterior distribution $\\pi (x|y)$? That is, how should you make a predi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1007.4532","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2010-07-26T19:03:11Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"040cb46f6bc047f18e43507548ef7911c0a31267d90dc55132db6589099b4fed","abstract_canon_sha256":"48894b9dae441f8615aed71af4f7e4fc2f4f18fe4c0d7d2d79a35ee1d6c4effb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:12.837007Z","signature_b64":"wMvwavcRiNr9zuM+TNphvwFkqh9+Ogmd3zumh/c2EiuiN3qeTeObWfhbFLPEKF/2O8Sjq/d9HFOFuK10JDMoDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91cc84a6adf96d05268986f51e4be79b444c1c11f75ce9df297fdc75ce4b0f85","last_reissued_at":"2026-05-18T02:17:12.835971Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:12.835971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A decision-theoretic approach for segmental classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Christopher C. Holmes, Christopher Yau","submitted_at":"2010-07-26T19:03:11Z","abstract_excerpt":"This paper is concerned with statistical methods for the segmental classification of linear sequence data where the task is to segment and classify the data according to an underlying hidden discrete state sequence. Such analysis is commonplace in the empirical sciences including genomics, finance and speech processing. In particular, we are interested in answering the following question: given data $y$ and a statistical model $\\pi(x,y)$ of the hidden states $x$, what should we report as the prediction $\\hat{x}$ under the posterior distribution $\\pi (x|y)$? That is, how should you make a predi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1007.4532","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1007.4532","created_at":"2026-05-18T02:17:12.836298+00:00"},{"alias_kind":"arxiv_version","alias_value":"1007.4532v2","created_at":"2026-05-18T02:17:12.836298+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1007.4532","created_at":"2026-05-18T02:17:12.836298+00:00"},{"alias_kind":"pith_short_12","alias_value":"SHGIJJVN7FWQ","created_at":"2026-05-18T12:26:13.927090+00:00"},{"alias_kind":"pith_short_16","alias_value":"SHGIJJVN7FWQKJUJ","created_at":"2026-05-18T12:26:13.927090+00:00"},{"alias_kind":"pith_short_8","alias_value":"SHGIJJVN","created_at":"2026-05-18T12:26:13.927090+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN","json":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN.json","graph_json":"https://pith.science/api/pith-number/SHGIJJVN7FWQKJUJQ32R4S7HTN/graph.json","events_json":"https://pith.science/api/pith-number/SHGIJJVN7FWQKJUJQ32R4S7HTN/events.json","paper":"https://pith.science/paper/SHGIJJVN"},"agent_actions":{"view_html":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN","download_json":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN.json","view_paper":"https://pith.science/paper/SHGIJJVN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1007.4532&json=true","fetch_graph":"https://pith.science/api/pith-number/SHGIJJVN7FWQKJUJQ32R4S7HTN/graph.json","fetch_events":"https://pith.science/api/pith-number/SHGIJJVN7FWQKJUJQ32R4S7HTN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN/action/storage_attestation","attest_author":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN/action/author_attestation","sign_citation":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN/action/citation_signature","submit_replication":"https://pith.science/pith/SHGIJJVN7FWQKJUJQ32R4S7HTN/action/replication_record"}},"created_at":"2026-05-18T02:17:12.836298+00:00","updated_at":"2026-05-18T02:17:12.836298+00:00"}