{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RKLT72MCGC73IUI5YDJT32JI4A","short_pith_number":"pith:RKLT72MC","schema_version":"1.0","canonical_sha256":"8a973fe98230bfb4511dc0d33de928e01c7cba2852d884c4fde5f05ecc792e8c","source":{"kind":"arxiv","id":"1605.00355","version":1},"attestation_state":"computed","paper":{"title":"Contrastive Structured Anomaly Detection for Gaussian Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Abhinav Maurya, Mark Cheung","submitted_at":"2016-05-02T05:42:10Z","abstract_excerpt":"Gaussian graphical models (GGMs) are probabilistic tools of choice for analyzing conditional dependencies between variables in complex systems. Finding changepoints in the structural evolution of a GGM is therefore essential to detecting anomalies in the underlying system modeled by the GGM. In order to detect structural anomalies in a GGM, we consider the problem of estimating changes in the precision matrix of the corresponding Gaussian distribution. We take a two-step approach to solving this problem:- (i) estimating a background precision matrix using system observations from the past with"},"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":"1605.00355","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-05-02T05:42:10Z","cross_cats_sorted":[],"title_canon_sha256":"82ff999ce20ff2f76b92fcbae86e9f24566cca105212ad15f7dd4c58c0779545","abstract_canon_sha256":"52e97a18d5303ea364d03c2625def7f82462f3fbd5dbabef7defccf4b8dc89b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:56.061438Z","signature_b64":"s3gUXeZfPimITRD71cRinJrkqa8/jHpO47lqeTqhQWj8lxVI44zOu3wY/RYNSDO6rTtZQThtOY6EJ03591WQDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a973fe98230bfb4511dc0d33de928e01c7cba2852d884c4fde5f05ecc792e8c","last_reissued_at":"2026-05-18T01:15:56.060657Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:56.060657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contrastive Structured Anomaly Detection for Gaussian Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Abhinav Maurya, Mark Cheung","submitted_at":"2016-05-02T05:42:10Z","abstract_excerpt":"Gaussian graphical models (GGMs) are probabilistic tools of choice for analyzing conditional dependencies between variables in complex systems. Finding changepoints in the structural evolution of a GGM is therefore essential to detecting anomalies in the underlying system modeled by the GGM. In order to detect structural anomalies in a GGM, we consider the problem of estimating changes in the precision matrix of the corresponding Gaussian distribution. We take a two-step approach to solving this problem:- (i) estimating a background precision matrix using system observations from the past with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.00355","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":"1605.00355","created_at":"2026-05-18T01:15:56.060772+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.00355v1","created_at":"2026-05-18T01:15:56.060772+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.00355","created_at":"2026-05-18T01:15:56.060772+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKLT72MCGC73","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKLT72MCGC73IUI5","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKLT72MC","created_at":"2026-05-18T12:30:41.710351+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/RKLT72MCGC73IUI5YDJT32JI4A","json":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A.json","graph_json":"https://pith.science/api/pith-number/RKLT72MCGC73IUI5YDJT32JI4A/graph.json","events_json":"https://pith.science/api/pith-number/RKLT72MCGC73IUI5YDJT32JI4A/events.json","paper":"https://pith.science/paper/RKLT72MC"},"agent_actions":{"view_html":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A","download_json":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A.json","view_paper":"https://pith.science/paper/RKLT72MC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.00355&json=true","fetch_graph":"https://pith.science/api/pith-number/RKLT72MCGC73IUI5YDJT32JI4A/graph.json","fetch_events":"https://pith.science/api/pith-number/RKLT72MCGC73IUI5YDJT32JI4A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/action/storage_attestation","attest_author":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/action/author_attestation","sign_citation":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/action/citation_signature","submit_replication":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/action/replication_record"}},"created_at":"2026-05-18T01:15:56.060772+00:00","updated_at":"2026-05-18T01:15:56.060772+00:00"}