{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:RCPC44AKYAQ7R7E3FJX2X6JPMJ","short_pith_number":"pith:RCPC44AK","canonical_record":{"source":{"id":"1711.00950","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-02T21:45:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fbe02929f284597c6862c8ceab25b2898628a57ecd2218d2df8bb3c84ff5ba10","abstract_canon_sha256":"eb7838354eb1229bb52b7f65f66d8a3af0dcb46d44b618f4aaed50f4b825deca"},"schema_version":"1.0"},"canonical_sha256":"889e2e700ac021f8fc9b2a6fabf92f626ba6affb594215eccc31881c5630c3dd","source":{"kind":"arxiv","id":"1711.00950","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00950","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00950v2","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00950","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"pith_short_12","alias_value":"RCPC44AKYAQ7","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RCPC44AKYAQ7R7E3","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RCPC44AK","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:RCPC44AKYAQ7R7E3FJX2X6JPMJ","target":"record","payload":{"canonical_record":{"source":{"id":"1711.00950","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-02T21:45:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fbe02929f284597c6862c8ceab25b2898628a57ecd2218d2df8bb3c84ff5ba10","abstract_canon_sha256":"eb7838354eb1229bb52b7f65f66d8a3af0dcb46d44b618f4aaed50f4b825deca"},"schema_version":"1.0"},"canonical_sha256":"889e2e700ac021f8fc9b2a6fabf92f626ba6affb594215eccc31881c5630c3dd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:18.347411Z","signature_b64":"4Bmbo+SHbtcaZh5/5HXKvs9EcC/Xv2bQTkSeucedSxNFh17IOCZbG6A1y4oy23SreOwdfgOGsZXjif5Uwg4WBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"889e2e700ac021f8fc9b2a6fabf92f626ba6affb594215eccc31881c5630c3dd","last_reissued_at":"2026-05-18T00:31:18.346789Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:18.346789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.00950","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":"2026-05-18T00:31:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Emts9SKGwYtagoJieF3+bG5OHAV7eKUxSDWGkPBCITmaiDcIL1XOoKhnG1mME4ZjfNcjn8Mxox26B3GcrUP9CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T17:14:50.163672Z"},"content_sha256":"c3741d1fb2e4ea3e838eec45cc25dfd92211f0b02b6bb2f39a037048b1a2d2a2","schema_version":"1.0","event_id":"sha256:c3741d1fb2e4ea3e838eec45cc25dfd92211f0b02b6bb2f39a037048b1a2d2a2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:RCPC44AKYAQ7R7E3FJX2X6JPMJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Rebecca E. Morrison, Ricardo Baptista, Youssef Marzouk","submitted_at":"2017-11-02T21:45:07Z","abstract_excerpt":"We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but most algorithms for learning this structure are restricted to the discrete or Gaussian cases. Our new approach allows for more realistic and accurate descriptions of the distribution in question, and in turn better estimates of its sparse Markov structure. Sparsity in the graph is of interest as it can"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00950","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":"2026-05-18T00:31:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P8DvYF/tb/jCj/HOhSvnbKAJ7PddY8cR37k4wzdbo6lzhOMJYLL9RA0LATVbsF31fABVAcv0VE4Zo4BBMie0CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T17:14:50.164011Z"},"content_sha256":"499f7a4dd48fa2ff8aef792ab984c011c8289619fbcecc58df518fa571a36d6b","schema_version":"1.0","event_id":"sha256:499f7a4dd48fa2ff8aef792ab984c011c8289619fbcecc58df518fa571a36d6b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RCPC44AKYAQ7R7E3FJX2X6JPMJ/bundle.json","state_url":"https://pith.science/pith/RCPC44AKYAQ7R7E3FJX2X6JPMJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RCPC44AKYAQ7R7E3FJX2X6JPMJ/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":"2026-06-26T17:14:50Z","links":{"resolver":"https://pith.science/pith/RCPC44AKYAQ7R7E3FJX2X6JPMJ","bundle":"https://pith.science/pith/RCPC44AKYAQ7R7E3FJX2X6JPMJ/bundle.json","state":"https://pith.science/pith/RCPC44AKYAQ7R7E3FJX2X6JPMJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RCPC44AKYAQ7R7E3FJX2X6JPMJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:RCPC44AKYAQ7R7E3FJX2X6JPMJ","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":"eb7838354eb1229bb52b7f65f66d8a3af0dcb46d44b618f4aaed50f4b825deca","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-02T21:45:07Z","title_canon_sha256":"fbe02929f284597c6862c8ceab25b2898628a57ecd2218d2df8bb3c84ff5ba10"},"schema_version":"1.0","source":{"id":"1711.00950","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00950","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00950v2","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00950","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"pith_short_12","alias_value":"RCPC44AKYAQ7","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RCPC44AKYAQ7R7E3","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RCPC44AK","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:499f7a4dd48fa2ff8aef792ab984c011c8289619fbcecc58df518fa571a36d6b","target":"graph","created_at":"2026-05-18T00:31:18Z","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":"We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but most algorithms for learning this structure are restricted to the discrete or Gaussian cases. Our new approach allows for more realistic and accurate descriptions of the distribution in question, and in turn better estimates of its sparse Markov structure. Sparsity in the graph is of interest as it can","authors_text":"Rebecca E. Morrison, Ricardo Baptista, Youssef Marzouk","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-02T21:45:07Z","title":"Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00950","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:c3741d1fb2e4ea3e838eec45cc25dfd92211f0b02b6bb2f39a037048b1a2d2a2","target":"record","created_at":"2026-05-18T00:31:18Z","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":"eb7838354eb1229bb52b7f65f66d8a3af0dcb46d44b618f4aaed50f4b825deca","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-02T21:45:07Z","title_canon_sha256":"fbe02929f284597c6862c8ceab25b2898628a57ecd2218d2df8bb3c84ff5ba10"},"schema_version":"1.0","source":{"id":"1711.00950","kind":"arxiv","version":2}},"canonical_sha256":"889e2e700ac021f8fc9b2a6fabf92f626ba6affb594215eccc31881c5630c3dd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"889e2e700ac021f8fc9b2a6fabf92f626ba6affb594215eccc31881c5630c3dd","first_computed_at":"2026-05-18T00:31:18.346789Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:18.346789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Bmbo+SHbtcaZh5/5HXKvs9EcC/Xv2bQTkSeucedSxNFh17IOCZbG6A1y4oy23SreOwdfgOGsZXjif5Uwg4WBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:18.347411Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.00950","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3741d1fb2e4ea3e838eec45cc25dfd92211f0b02b6bb2f39a037048b1a2d2a2","sha256:499f7a4dd48fa2ff8aef792ab984c011c8289619fbcecc58df518fa571a36d6b"],"state_sha256":"7e9e5882ac89bdffb6d82c30a065cb34d5348b41152559408c12cb2afb68f3f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2ZA/BTSfJBdHt37HzNLD8MJYkVSAV+xMG7cIVju5HnQAJa26cLFtdgHKr3lKI5m2YIQ7PqQoK0CpEYX+04rqCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T17:14:50.165825Z","bundle_sha256":"4a0207346c5e9d742c60c740a5248a03ab3c63bae1217931e19b47a385feed51"}}