{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:MQUAF5SCARFJHN6UGQDK2VSFKT","short_pith_number":"pith:MQUAF5SC","schema_version":"1.0","canonical_sha256":"642802f642044a93b7d43406ad564554f9378e3af6a4ee051738f113950af836","source":{"kind":"arxiv","id":"1204.5540","version":3},"attestation_state":"computed","paper":{"title":"Learning Loosely Connected Markov Random Fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Jian Ni, R. Srikant, Rui Wu","submitted_at":"2012-04-25T02:39:40Z","abstract_excerpt":"We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure. The novel maximization step in our algorithm ensures that the true edges are detected correctly even when there are short cycles in the graph. The number of samples required by our algorithm is C*log p, where p is the size of the graph and the constant C depends on the parameters of the model. We show "},"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":"1204.5540","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-04-25T02:39:40Z","cross_cats_sorted":[],"title_canon_sha256":"667f1b901edc5468ff51fed53db8475662c32dbba22bbfd74da5001eb329467e","abstract_canon_sha256":"fb6ced0e7d5d22e14450a23b9ec16b3f7e6d840d92e894d850de42d1e08febbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:00:13.021905Z","signature_b64":"fkvRtEQOgaQ85IZuKQHRzTxTSjuDEneuFOhFx7DG5zLJw9nozBnwwdKCnu5STqOScPCHdQCd40b3FgtWfVdACQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"642802f642044a93b7d43406ad564554f9378e3af6a4ee051738f113950af836","last_reissued_at":"2026-05-18T03:00:13.021135Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:00:13.021135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Loosely Connected Markov Random Fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Jian Ni, R. Srikant, Rui Wu","submitted_at":"2012-04-25T02:39:40Z","abstract_excerpt":"We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure. The novel maximization step in our algorithm ensures that the true edges are detected correctly even when there are short cycles in the graph. The number of samples required by our algorithm is C*log p, where p is the size of the graph and the constant C depends on the parameters of the model. We show "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1204.5540","kind":"arxiv","version":3},"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":"1204.5540","created_at":"2026-05-18T03:00:13.021256+00:00"},{"alias_kind":"arxiv_version","alias_value":"1204.5540v3","created_at":"2026-05-18T03:00:13.021256+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1204.5540","created_at":"2026-05-18T03:00:13.021256+00:00"},{"alias_kind":"pith_short_12","alias_value":"MQUAF5SCARFJ","created_at":"2026-05-18T12:27:14.488303+00:00"},{"alias_kind":"pith_short_16","alias_value":"MQUAF5SCARFJHN6U","created_at":"2026-05-18T12:27:14.488303+00:00"},{"alias_kind":"pith_short_8","alias_value":"MQUAF5SC","created_at":"2026-05-18T12:27:14.488303+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/MQUAF5SCARFJHN6UGQDK2VSFKT","json":"https://pith.science/pith/MQUAF5SCARFJHN6UGQDK2VSFKT.json","graph_json":"https://pith.science/api/pith-number/MQUAF5SCARFJHN6UGQDK2VSFKT/graph.json","events_json":"https://pith.science/api/pith-number/MQUAF5SCARFJHN6UGQDK2VSFKT/events.json","paper":"https://pith.science/paper/MQUAF5SC"},"agent_actions":{"view_html":"https://pith.science/pith/MQUAF5SCARFJHN6UGQDK2VSFKT","download_json":"https://pith.science/pith/MQUAF5SCARFJHN6UGQDK2VSFKT.json","view_paper":"https://pith.science/paper/MQUAF5SC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1204.5540&json=true","fetch_graph":"https://pith.science/api/pith-number/MQUAF5SCARFJHN6UGQDK2VSFKT/graph.json","fetch_events":"https://pith.science/api/pith-number/MQUAF5SCARFJHN6UGQDK2VSFKT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MQUAF5SCARFJHN6UGQDK2VSFKT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MQUAF5SCARFJHN6UGQDK2VSFKT/action/storage_attestation","attest_author":"https://pith.science/pith/MQUAF5SCARFJHN6UGQDK2VSFKT/action/author_attestation","sign_citation":"https://pith.science/pith/MQUAF5SCARFJHN6UGQDK2VSFKT/action/citation_signature","submit_replication":"https://pith.science/pith/MQUAF5SCARFJHN6UGQDK2VSFKT/action/replication_record"}},"created_at":"2026-05-18T03:00:13.021256+00:00","updated_at":"2026-05-18T03:00:13.021256+00:00"}