{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:X3WYXGL54CCP2RU66TFWCRHKK5","short_pith_number":"pith:X3WYXGL5","canonical_record":{"source":{"id":"1807.07754","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-20T09:35:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"50a93dffd21d0adc7509fbc7c50ba91ee4600fbdc3f368bf013b7952b1201934","abstract_canon_sha256":"b6ffacd52044787da5e6d5e72f140ad0027917e465090c7fc7190f415b5c5ea9"},"schema_version":"1.0"},"canonical_sha256":"beed8b997de084fd469ef4cb6144ea576cc4093dee20418993ece305e793c314","source":{"kind":"arxiv","id":"1807.07754","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.07754","created_at":"2026-05-18T00:09:23Z"},{"alias_kind":"arxiv_version","alias_value":"1807.07754v2","created_at":"2026-05-18T00:09:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.07754","created_at":"2026-05-18T00:09:23Z"},{"alias_kind":"pith_short_12","alias_value":"X3WYXGL54CCP","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"X3WYXGL54CCP2RU6","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"X3WYXGL5","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:X3WYXGL54CCP2RU66TFWCRHKK5","target":"record","payload":{"canonical_record":{"source":{"id":"1807.07754","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-20T09:35:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"50a93dffd21d0adc7509fbc7c50ba91ee4600fbdc3f368bf013b7952b1201934","abstract_canon_sha256":"b6ffacd52044787da5e6d5e72f140ad0027917e465090c7fc7190f415b5c5ea9"},"schema_version":"1.0"},"canonical_sha256":"beed8b997de084fd469ef4cb6144ea576cc4093dee20418993ece305e793c314","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:23.837306Z","signature_b64":"jYVyGSdCQDrXLn6mQzKhpt3G//7o0pxoUXc8olgrUaUkdtIywKG24ZFcmYwjBJ3Lo3wMlCBKlgkahZXPTXCCDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"beed8b997de084fd469ef4cb6144ea576cc4093dee20418993ece305e793c314","last_reissued_at":"2026-05-18T00:09:23.834736Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:23.834736Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.07754","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:09:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IBry9xBidhncuK6DhPiV2OhRjc7WGvhowsuSWjLbfWitQspL0735yA/rUOAWBCnDv/GnCsUJI6vkWjkAJMgBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T17:26:26.319454Z"},"content_sha256":"1eaa32eca52b9f6992ab5d72a7f75ea24a94e0dfbff33ad2d8b037b2a620b5da","schema_version":"1.0","event_id":"sha256:1eaa32eca52b9f6992ab5d72a7f75ea24a94e0dfbff33ad2d8b037b2a620b5da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:X3WYXGL54CCP2RU66TFWCRHKK5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning the effect of latent variables in Gaussian Graphical models with unobserved variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Guillaume Obozinski, Marina Vinyes","submitted_at":"2018-07-20T09:35:25Z","abstract_excerpt":"The edge structure of the graph defining an undirected graphical model describes precisely the structure of dependence between the variables in the graph. In many applications, the dependence structure is unknown and it is desirable to learn it from data, often because it is a preliminary step to be able to ascertain causal effects. This problem, known as structure learning, is hard in general, but for Gaussian graphical models it is slightly easier because the structure of the graph is given by the sparsity pattern of the precision matrix of the joint distribution, and because independence co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.07754","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:09:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tBj0B2HqTtUBha20vT0OLU+pLGvHy2Lbti6qVVQg0Ri7of0DPv6jSkjxCgU9AGgJPORdv4IM/JNjf7OcxeMnBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T17:26:26.319854Z"},"content_sha256":"66ca169037c82841e0025901cc589673699cd1396e44fa798490c210b9f3b792","schema_version":"1.0","event_id":"sha256:66ca169037c82841e0025901cc589673699cd1396e44fa798490c210b9f3b792"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X3WYXGL54CCP2RU66TFWCRHKK5/bundle.json","state_url":"https://pith.science/pith/X3WYXGL54CCP2RU66TFWCRHKK5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X3WYXGL54CCP2RU66TFWCRHKK5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T17:26:26Z","links":{"resolver":"https://pith.science/pith/X3WYXGL54CCP2RU66TFWCRHKK5","bundle":"https://pith.science/pith/X3WYXGL54CCP2RU66TFWCRHKK5/bundle.json","state":"https://pith.science/pith/X3WYXGL54CCP2RU66TFWCRHKK5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X3WYXGL54CCP2RU66TFWCRHKK5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:X3WYXGL54CCP2RU66TFWCRHKK5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b6ffacd52044787da5e6d5e72f140ad0027917e465090c7fc7190f415b5c5ea9","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-20T09:35:25Z","title_canon_sha256":"50a93dffd21d0adc7509fbc7c50ba91ee4600fbdc3f368bf013b7952b1201934"},"schema_version":"1.0","source":{"id":"1807.07754","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.07754","created_at":"2026-05-18T00:09:23Z"},{"alias_kind":"arxiv_version","alias_value":"1807.07754v2","created_at":"2026-05-18T00:09:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.07754","created_at":"2026-05-18T00:09:23Z"},{"alias_kind":"pith_short_12","alias_value":"X3WYXGL54CCP","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"X3WYXGL54CCP2RU6","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"X3WYXGL5","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:66ca169037c82841e0025901cc589673699cd1396e44fa798490c210b9f3b792","target":"graph","created_at":"2026-05-18T00:09:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The edge structure of the graph defining an undirected graphical model describes precisely the structure of dependence between the variables in the graph. In many applications, the dependence structure is unknown and it is desirable to learn it from data, often because it is a preliminary step to be able to ascertain causal effects. This problem, known as structure learning, is hard in general, but for Gaussian graphical models it is slightly easier because the structure of the graph is given by the sparsity pattern of the precision matrix of the joint distribution, and because independence co","authors_text":"Guillaume Obozinski, Marina Vinyes","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-20T09:35:25Z","title":"Learning the effect of latent variables in Gaussian Graphical models with unobserved variables"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.07754","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1eaa32eca52b9f6992ab5d72a7f75ea24a94e0dfbff33ad2d8b037b2a620b5da","target":"record","created_at":"2026-05-18T00:09:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b6ffacd52044787da5e6d5e72f140ad0027917e465090c7fc7190f415b5c5ea9","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-20T09:35:25Z","title_canon_sha256":"50a93dffd21d0adc7509fbc7c50ba91ee4600fbdc3f368bf013b7952b1201934"},"schema_version":"1.0","source":{"id":"1807.07754","kind":"arxiv","version":2}},"canonical_sha256":"beed8b997de084fd469ef4cb6144ea576cc4093dee20418993ece305e793c314","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"beed8b997de084fd469ef4cb6144ea576cc4093dee20418993ece305e793c314","first_computed_at":"2026-05-18T00:09:23.834736Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:23.834736Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jYVyGSdCQDrXLn6mQzKhpt3G//7o0pxoUXc8olgrUaUkdtIywKG24ZFcmYwjBJ3Lo3wMlCBKlgkahZXPTXCCDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:23.837306Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.07754","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1eaa32eca52b9f6992ab5d72a7f75ea24a94e0dfbff33ad2d8b037b2a620b5da","sha256:66ca169037c82841e0025901cc589673699cd1396e44fa798490c210b9f3b792"],"state_sha256":"22499634da6f9ac61c856bb3670301909f94f31629863ecc5a09306d9583897e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QrByOPUm2lvlx0YHK/5SObapk63rGrjpfGD9wahRXQhekORpJqyQtnis90vyMOO5cZwRKBlhpCz1BJkFf0P+CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T17:26:26.322290Z","bundle_sha256":"5c0672a6342238f219fb79711fbf6af257030269891235999bf27c2c96ab70cd"}}