{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2009:SVGNDI4SABMRIF7UUZ7L6HY3LH","short_pith_number":"pith:SVGNDI4S","schema_version":"1.0","canonical_sha256":"954cd1a39200591417f4a67ebf1f1b59e4d8c9934dd4ead4ad683a711d929e89","source":{"kind":"arxiv","id":"0909.0737","version":2},"attestation_state":"computed","paper":{"title":"Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization EM training and Viterbi training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.GN"],"primary_cat":"q-bio.QM","authors_text":"Irmtraud M. Meyer, Tin Yin Lam","submitted_at":"2009-09-03T19:29:56Z","abstract_excerpt":"Background: Hidden Markov models are widely employed by numerous bioinformatics programs used today. Applications range widely from comparative gene prediction to time-series analyses of micro-array data. The parameters of the underlying models need to be adjusted for specific data sets, for example the genome of a particular species, in order to maximize the prediction accuracy. Computationally efficient algorithms for parameter training are thus key to maximizing the usability of a wide range of bioinformatics applications.\n  Results: We introduce two computationally efficient training algor"},"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":"0909.0737","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2009-09-03T19:29:56Z","cross_cats_sorted":["cs.LG","q-bio.GN"],"title_canon_sha256":"177d6d004ef0dd65cc66020941fba107b6009c7290dded12eb9d565c79cc4e98","abstract_canon_sha256":"a4c061f7138961279ca9b04cbd75c98d02ca91befdcb888104d0688c142a576d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:43:05.499344Z","signature_b64":"ajZNxXol/GYGCxtyH4H5iqMAjWHVArzt3CMu15D43exrfpO58KuXwxXyDnN63zAUIfkFDbk33lLB9wZ3u8VpCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"954cd1a39200591417f4a67ebf1f1b59e4d8c9934dd4ead4ad683a711d929e89","last_reissued_at":"2026-05-18T03:43:05.498687Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:43:05.498687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization EM training and Viterbi training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.GN"],"primary_cat":"q-bio.QM","authors_text":"Irmtraud M. Meyer, Tin Yin Lam","submitted_at":"2009-09-03T19:29:56Z","abstract_excerpt":"Background: Hidden Markov models are widely employed by numerous bioinformatics programs used today. Applications range widely from comparative gene prediction to time-series analyses of micro-array data. The parameters of the underlying models need to be adjusted for specific data sets, for example the genome of a particular species, in order to maximize the prediction accuracy. Computationally efficient algorithms for parameter training are thus key to maximizing the usability of a wide range of bioinformatics applications.\n  Results: We introduce two computationally efficient training algor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0909.0737","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":"0909.0737","created_at":"2026-05-18T03:43:05.498788+00:00"},{"alias_kind":"arxiv_version","alias_value":"0909.0737v2","created_at":"2026-05-18T03:43:05.498788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0909.0737","created_at":"2026-05-18T03:43:05.498788+00:00"},{"alias_kind":"pith_short_12","alias_value":"SVGNDI4SABMR","created_at":"2026-05-18T12:26:01.383474+00:00"},{"alias_kind":"pith_short_16","alias_value":"SVGNDI4SABMRIF7U","created_at":"2026-05-18T12:26:01.383474+00:00"},{"alias_kind":"pith_short_8","alias_value":"SVGNDI4S","created_at":"2026-05-18T12:26:01.383474+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/SVGNDI4SABMRIF7UUZ7L6HY3LH","json":"https://pith.science/pith/SVGNDI4SABMRIF7UUZ7L6HY3LH.json","graph_json":"https://pith.science/api/pith-number/SVGNDI4SABMRIF7UUZ7L6HY3LH/graph.json","events_json":"https://pith.science/api/pith-number/SVGNDI4SABMRIF7UUZ7L6HY3LH/events.json","paper":"https://pith.science/paper/SVGNDI4S"},"agent_actions":{"view_html":"https://pith.science/pith/SVGNDI4SABMRIF7UUZ7L6HY3LH","download_json":"https://pith.science/pith/SVGNDI4SABMRIF7UUZ7L6HY3LH.json","view_paper":"https://pith.science/paper/SVGNDI4S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0909.0737&json=true","fetch_graph":"https://pith.science/api/pith-number/SVGNDI4SABMRIF7UUZ7L6HY3LH/graph.json","fetch_events":"https://pith.science/api/pith-number/SVGNDI4SABMRIF7UUZ7L6HY3LH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SVGNDI4SABMRIF7UUZ7L6HY3LH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SVGNDI4SABMRIF7UUZ7L6HY3LH/action/storage_attestation","attest_author":"https://pith.science/pith/SVGNDI4SABMRIF7UUZ7L6HY3LH/action/author_attestation","sign_citation":"https://pith.science/pith/SVGNDI4SABMRIF7UUZ7L6HY3LH/action/citation_signature","submit_replication":"https://pith.science/pith/SVGNDI4SABMRIF7UUZ7L6HY3LH/action/replication_record"}},"created_at":"2026-05-18T03:43:05.498788+00:00","updated_at":"2026-05-18T03:43:05.498788+00:00"}