{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:X4MCB26Y4XBT6K6XWT5KEDBJY5","short_pith_number":"pith:X4MCB26Y","schema_version":"1.0","canonical_sha256":"bf1820ebd8e5c33f2bd7b4faa20c29c76c20d52e82d966ff2cb2e85ad0535ba5","source":{"kind":"arxiv","id":"1402.0577","version":1},"attestation_state":"computed","paper":{"title":"A Survey on Latent Tree Models and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christine Sinoquet, Nevin L. Zhang, Philippe Leray, Rapha\\\"el Mourad, Tengfei Liu","submitted_at":"2014-02-04T01:40:28Z","abstract_excerpt":"In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summari"},"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":"1402.0577","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-02-04T01:40:28Z","cross_cats_sorted":[],"title_canon_sha256":"3b00e7882b404c4d704d0df4b7223ccef6dbf9295b9a46c397d6fad18e1dc2c9","abstract_canon_sha256":"29a3bc89175fd61d34b36afeb4a749485ad03d61c40786885c87b2b1b618f22d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:00:13.782424Z","signature_b64":"HaeLsWZGsrKQfM82AUD71PEJE7L44mQLIorOrd9M5pHuX3X0Er6c44miLmpljekG51IA0l+d+T+NlrDZDPriBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf1820ebd8e5c33f2bd7b4faa20c29c76c20d52e82d966ff2cb2e85ad0535ba5","last_reissued_at":"2026-05-18T03:00:13.781784Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:00:13.781784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Latent Tree Models and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christine Sinoquet, Nevin L. Zhang, Philippe Leray, Rapha\\\"el Mourad, Tengfei Liu","submitted_at":"2014-02-04T01:40:28Z","abstract_excerpt":"In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summari"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.0577","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1402.0577","created_at":"2026-05-18T03:00:13.781909+00:00"},{"alias_kind":"arxiv_version","alias_value":"1402.0577v1","created_at":"2026-05-18T03:00:13.781909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.0577","created_at":"2026-05-18T03:00:13.781909+00:00"},{"alias_kind":"pith_short_12","alias_value":"X4MCB26Y4XBT","created_at":"2026-05-18T12:28:54.890064+00:00"},{"alias_kind":"pith_short_16","alias_value":"X4MCB26Y4XBT6K6X","created_at":"2026-05-18T12:28:54.890064+00:00"},{"alias_kind":"pith_short_8","alias_value":"X4MCB26Y","created_at":"2026-05-18T12:28:54.890064+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/X4MCB26Y4XBT6K6XWT5KEDBJY5","json":"https://pith.science/pith/X4MCB26Y4XBT6K6XWT5KEDBJY5.json","graph_json":"https://pith.science/api/pith-number/X4MCB26Y4XBT6K6XWT5KEDBJY5/graph.json","events_json":"https://pith.science/api/pith-number/X4MCB26Y4XBT6K6XWT5KEDBJY5/events.json","paper":"https://pith.science/paper/X4MCB26Y"},"agent_actions":{"view_html":"https://pith.science/pith/X4MCB26Y4XBT6K6XWT5KEDBJY5","download_json":"https://pith.science/pith/X4MCB26Y4XBT6K6XWT5KEDBJY5.json","view_paper":"https://pith.science/paper/X4MCB26Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1402.0577&json=true","fetch_graph":"https://pith.science/api/pith-number/X4MCB26Y4XBT6K6XWT5KEDBJY5/graph.json","fetch_events":"https://pith.science/api/pith-number/X4MCB26Y4XBT6K6XWT5KEDBJY5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X4MCB26Y4XBT6K6XWT5KEDBJY5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X4MCB26Y4XBT6K6XWT5KEDBJY5/action/storage_attestation","attest_author":"https://pith.science/pith/X4MCB26Y4XBT6K6XWT5KEDBJY5/action/author_attestation","sign_citation":"https://pith.science/pith/X4MCB26Y4XBT6K6XWT5KEDBJY5/action/citation_signature","submit_replication":"https://pith.science/pith/X4MCB26Y4XBT6K6XWT5KEDBJY5/action/replication_record"}},"created_at":"2026-05-18T03:00:13.781909+00:00","updated_at":"2026-05-18T03:00:13.781909+00:00"}