{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:ZVBYRMT33DVU7YJ54WLO6LDFZY","short_pith_number":"pith:ZVBYRMT3","schema_version":"1.0","canonical_sha256":"cd4388b27bd8eb4fe13de596ef2c65ce26c8d911a943e2b45d3e9e555d1d7c5e","source":{"kind":"arxiv","id":"1202.3765","version":1},"attestation_state":"computed","paper":{"title":"Learning mixed graphical models from data with p larger than n","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"stat.ME","authors_text":"Inma Tur, Robert Castelo","submitted_at":"2012-02-14T16:41:17Z","abstract_excerpt":"Structure learning of Gaussian graphical models is an extensively studied problem in the classical multivariate setting where the sample size n is larger than the number of random variables p, as well as in the more challenging setting when p>>n. However, analogous approaches for learning the structure of graphical models with mixed discrete and continuous variables when p>>n remain largely unexplored. Here we describe a statistical learning procedure for this problem based on limited-order correlations and assess its performance with synthetic and real data."},"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":"1202.3765","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-02-14T16:41:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"0c475321ab7d1b204375bd64d0ff26d060a2c41c1f06a215b19e6f671c977d0d","abstract_canon_sha256":"da63841095f2128a0a8a8c705c13d2e844991a0e6de3e6f914b6681a4e131976"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:02:02.960783Z","signature_b64":"WrODMnezd8IIGkRemNYmOZDiGcB1aFO03puGpKgFwuXO8Uf1ojS0Cs8ji6DPHqxIC5ePiOr96TLaBBKbDSP+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd4388b27bd8eb4fe13de596ef2c65ce26c8d911a943e2b45d3e9e555d1d7c5e","last_reissued_at":"2026-05-18T04:02:02.960025Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:02:02.960025Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning mixed graphical models from data with p larger than n","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"stat.ME","authors_text":"Inma Tur, Robert Castelo","submitted_at":"2012-02-14T16:41:17Z","abstract_excerpt":"Structure learning of Gaussian graphical models is an extensively studied problem in the classical multivariate setting where the sample size n is larger than the number of random variables p, as well as in the more challenging setting when p>>n. However, analogous approaches for learning the structure of graphical models with mixed discrete and continuous variables when p>>n remain largely unexplored. Here we describe a statistical learning procedure for this problem based on limited-order correlations and assess its performance with synthetic and real data."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1202.3765","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":"1202.3765","created_at":"2026-05-18T04:02:02.960131+00:00"},{"alias_kind":"arxiv_version","alias_value":"1202.3765v1","created_at":"2026-05-18T04:02:02.960131+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1202.3765","created_at":"2026-05-18T04:02:02.960131+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZVBYRMT33DVU","created_at":"2026-05-18T12:27:30.460161+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZVBYRMT33DVU7YJ5","created_at":"2026-05-18T12:27:30.460161+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZVBYRMT3","created_at":"2026-05-18T12:27:30.460161+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/ZVBYRMT33DVU7YJ54WLO6LDFZY","json":"https://pith.science/pith/ZVBYRMT33DVU7YJ54WLO6LDFZY.json","graph_json":"https://pith.science/api/pith-number/ZVBYRMT33DVU7YJ54WLO6LDFZY/graph.json","events_json":"https://pith.science/api/pith-number/ZVBYRMT33DVU7YJ54WLO6LDFZY/events.json","paper":"https://pith.science/paper/ZVBYRMT3"},"agent_actions":{"view_html":"https://pith.science/pith/ZVBYRMT33DVU7YJ54WLO6LDFZY","download_json":"https://pith.science/pith/ZVBYRMT33DVU7YJ54WLO6LDFZY.json","view_paper":"https://pith.science/paper/ZVBYRMT3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1202.3765&json=true","fetch_graph":"https://pith.science/api/pith-number/ZVBYRMT33DVU7YJ54WLO6LDFZY/graph.json","fetch_events":"https://pith.science/api/pith-number/ZVBYRMT33DVU7YJ54WLO6LDFZY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZVBYRMT33DVU7YJ54WLO6LDFZY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZVBYRMT33DVU7YJ54WLO6LDFZY/action/storage_attestation","attest_author":"https://pith.science/pith/ZVBYRMT33DVU7YJ54WLO6LDFZY/action/author_attestation","sign_citation":"https://pith.science/pith/ZVBYRMT33DVU7YJ54WLO6LDFZY/action/citation_signature","submit_replication":"https://pith.science/pith/ZVBYRMT33DVU7YJ54WLO6LDFZY/action/replication_record"}},"created_at":"2026-05-18T04:02:02.960131+00:00","updated_at":"2026-05-18T04:02:02.960131+00:00"}