{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:SZW4MUAABKWUJUXXDFT6SR6KUW","short_pith_number":"pith:SZW4MUAA","schema_version":"1.0","canonical_sha256":"966dc650000aad44d2f71967e947caa595987cbcd070137e9b2cf2d2437cd4f5","source":{"kind":"arxiv","id":"1707.07560","version":1},"attestation_state":"computed","paper":{"title":"Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Marco F. Eigenmann, Marloes H. Maathuis, Preetam Nandy","submitted_at":"2017-07-24T13:56:48Z","abstract_excerpt":"We consider structure learning of linear Gaussian structural equation models with weak edges. Since the presence of weak edges can lead to a loss of edge orientations in the true underlying CPDAG, we define a new graphical object that can contain more edge orientations. We show that this object can be recovered from observational data under a type of strong faithfulness assumption. We present a new algorithm for this purpose, called aggregated greedy equivalence search (AGES), that aggregates the solution path of the greedy equivalence search (GES) algorithm for varying values of the penalty p"},"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":"1707.07560","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-07-24T13:56:48Z","cross_cats_sorted":[],"title_canon_sha256":"9f8b13309d2fd2f88c732d4601478f2d6c796c253b5382a960068f02e2511bd1","abstract_canon_sha256":"d668110b74121ed0ad570fbfcb6733563847db93ae35534ca3ce98acf8211307"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:41.848103Z","signature_b64":"DHKQQPzJEnwnzfd4fmeMCeuS3zzkiZd4NV7CLioYsWHQgLrr9unsA8xlmqBsLOWcGsU2AkTFr0x078EUM0l6AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"966dc650000aad44d2f71967e947caa595987cbcd070137e9b2cf2d2437cd4f5","last_reissued_at":"2026-05-18T00:39:41.847467Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:41.847467Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Marco F. Eigenmann, Marloes H. Maathuis, Preetam Nandy","submitted_at":"2017-07-24T13:56:48Z","abstract_excerpt":"We consider structure learning of linear Gaussian structural equation models with weak edges. Since the presence of weak edges can lead to a loss of edge orientations in the true underlying CPDAG, we define a new graphical object that can contain more edge orientations. We show that this object can be recovered from observational data under a type of strong faithfulness assumption. We present a new algorithm for this purpose, called aggregated greedy equivalence search (AGES), that aggregates the solution path of the greedy equivalence search (GES) algorithm for varying values of the penalty p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07560","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":"1707.07560","created_at":"2026-05-18T00:39:41.847562+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.07560v1","created_at":"2026-05-18T00:39:41.847562+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07560","created_at":"2026-05-18T00:39:41.847562+00:00"},{"alias_kind":"pith_short_12","alias_value":"SZW4MUAABKWU","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"SZW4MUAABKWUJUXX","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"SZW4MUAA","created_at":"2026-05-18T12:31:43.269735+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/SZW4MUAABKWUJUXXDFT6SR6KUW","json":"https://pith.science/pith/SZW4MUAABKWUJUXXDFT6SR6KUW.json","graph_json":"https://pith.science/api/pith-number/SZW4MUAABKWUJUXXDFT6SR6KUW/graph.json","events_json":"https://pith.science/api/pith-number/SZW4MUAABKWUJUXXDFT6SR6KUW/events.json","paper":"https://pith.science/paper/SZW4MUAA"},"agent_actions":{"view_html":"https://pith.science/pith/SZW4MUAABKWUJUXXDFT6SR6KUW","download_json":"https://pith.science/pith/SZW4MUAABKWUJUXXDFT6SR6KUW.json","view_paper":"https://pith.science/paper/SZW4MUAA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.07560&json=true","fetch_graph":"https://pith.science/api/pith-number/SZW4MUAABKWUJUXXDFT6SR6KUW/graph.json","fetch_events":"https://pith.science/api/pith-number/SZW4MUAABKWUJUXXDFT6SR6KUW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SZW4MUAABKWUJUXXDFT6SR6KUW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SZW4MUAABKWUJUXXDFT6SR6KUW/action/storage_attestation","attest_author":"https://pith.science/pith/SZW4MUAABKWUJUXXDFT6SR6KUW/action/author_attestation","sign_citation":"https://pith.science/pith/SZW4MUAABKWUJUXXDFT6SR6KUW/action/citation_signature","submit_replication":"https://pith.science/pith/SZW4MUAABKWUJUXXDFT6SR6KUW/action/replication_record"}},"created_at":"2026-05-18T00:39:41.847562+00:00","updated_at":"2026-05-18T00:39:41.847562+00:00"}