{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:Y4FKDMLJLWFIVYVNITX433F7DJ","short_pith_number":"pith:Y4FKDMLJ","canonical_record":{"source":{"id":"1602.07863","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-25T09:42:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4a4e2fa1d7ae9ee7f821cf20f23ccc74b61d2cec25ac19cc5d55c2b4e2b85c22","abstract_canon_sha256":"2490a6c2baada51c28e0696a0e49333fa6daefe218ddc53f2934dd7db83f2db9"},"schema_version":"1.0"},"canonical_sha256":"c70aa1b1695d8a8ae2ad44efcdecbf1a7581956f1d95552b09d2f348e6253aa7","source":{"kind":"arxiv","id":"1602.07863","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.07863","created_at":"2026-05-18T00:46:31Z"},{"alias_kind":"arxiv_version","alias_value":"1602.07863v1","created_at":"2026-05-18T00:46:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.07863","created_at":"2026-05-18T00:46:31Z"},{"alias_kind":"pith_short_12","alias_value":"Y4FKDMLJLWFI","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"Y4FKDMLJLWFIVYVN","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"Y4FKDMLJ","created_at":"2026-05-18T12:30:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:Y4FKDMLJLWFIVYVNITX433F7DJ","target":"record","payload":{"canonical_record":{"source":{"id":"1602.07863","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-25T09:42:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4a4e2fa1d7ae9ee7f821cf20f23ccc74b61d2cec25ac19cc5d55c2b4e2b85c22","abstract_canon_sha256":"2490a6c2baada51c28e0696a0e49333fa6daefe218ddc53f2934dd7db83f2db9"},"schema_version":"1.0"},"canonical_sha256":"c70aa1b1695d8a8ae2ad44efcdecbf1a7581956f1d95552b09d2f348e6253aa7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:31.986521Z","signature_b64":"pAdoULXPzD6LLtRCJFOWIUQVB1dh8l3uurjGrMulAhO6rQgApFVqHt7JyM5ERBk794k+k/iiz3/QxVNEstYoBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c70aa1b1695d8a8ae2ad44efcdecbf1a7581956f1d95552b09d2f348e6253aa7","last_reissued_at":"2026-05-18T00:46:31.985746Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:31.985746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.07863","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:46:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DCNni49Zd5H+YCB3H6fMx2LtgekCQpwG0qVnDOVBh4J+qxS7Kb/FTwhwdbvruVckgcoqRuC1sGW6GsTPczp7Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:00:51.088080Z"},"content_sha256":"be3e44fabd3cb922da50f0e79403335d14d4431a9c8cb3718541a269edaca0fe","schema_version":"1.0","event_id":"sha256:be3e44fabd3cb922da50f0e79403335d14d4431a9c8cb3718541a269edaca0fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:Y4FKDMLJLWFIVYVNITX433F7DJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Janne Lepp\\\"a-aho, Johan Pensar, Jukka Corander, Teemu Roos","submitted_at":"2016-02-25T09:42:46Z","abstract_excerpt":"We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression to approximate the marginal likelihood for an arbitrary graph structure without invoking any assumptions about decomposability. The majority of the existing methods for learning Gaussian graphical models are either restricted to decomposable graphs or require specification of a tuning parameter that may have a substantial impact on learned structures. By combining a simple sparsity inducing prior for the graph struct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.07863","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:46:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"elQSp7Mwpx55C9/24fjZnKSsQvJjePLl17Zx7d7wX9/aaaSHoq4Q2xT49SaabrWqndav4eUJ13Q3QofWmCWOBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:00:51.088802Z"},"content_sha256":"abc47b13aed977bf8a702d946417933319b47bd42f1d950576f1f0344ede8b2b","schema_version":"1.0","event_id":"sha256:abc47b13aed977bf8a702d946417933319b47bd42f1d950576f1f0344ede8b2b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y4FKDMLJLWFIVYVNITX433F7DJ/bundle.json","state_url":"https://pith.science/pith/Y4FKDMLJLWFIVYVNITX433F7DJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y4FKDMLJLWFIVYVNITX433F7DJ/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":"2026-05-26T11:00:51Z","links":{"resolver":"https://pith.science/pith/Y4FKDMLJLWFIVYVNITX433F7DJ","bundle":"https://pith.science/pith/Y4FKDMLJLWFIVYVNITX433F7DJ/bundle.json","state":"https://pith.science/pith/Y4FKDMLJLWFIVYVNITX433F7DJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y4FKDMLJLWFIVYVNITX433F7DJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:Y4FKDMLJLWFIVYVNITX433F7DJ","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":"2490a6c2baada51c28e0696a0e49333fa6daefe218ddc53f2934dd7db83f2db9","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-25T09:42:46Z","title_canon_sha256":"4a4e2fa1d7ae9ee7f821cf20f23ccc74b61d2cec25ac19cc5d55c2b4e2b85c22"},"schema_version":"1.0","source":{"id":"1602.07863","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.07863","created_at":"2026-05-18T00:46:31Z"},{"alias_kind":"arxiv_version","alias_value":"1602.07863v1","created_at":"2026-05-18T00:46:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.07863","created_at":"2026-05-18T00:46:31Z"},{"alias_kind":"pith_short_12","alias_value":"Y4FKDMLJLWFI","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"Y4FKDMLJLWFIVYVN","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"Y4FKDMLJ","created_at":"2026-05-18T12:30:51Z"}],"graph_snapshots":[{"event_id":"sha256:abc47b13aed977bf8a702d946417933319b47bd42f1d950576f1f0344ede8b2b","target":"graph","created_at":"2026-05-18T00:46:31Z","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 propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression to approximate the marginal likelihood for an arbitrary graph structure without invoking any assumptions about decomposability. The majority of the existing methods for learning Gaussian graphical models are either restricted to decomposable graphs or require specification of a tuning parameter that may have a substantial impact on learned structures. By combining a simple sparsity inducing prior for the graph struct","authors_text":"Janne Lepp\\\"a-aho, Johan Pensar, Jukka Corander, Teemu Roos","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-25T09:42:46Z","title":"Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.07863","kind":"arxiv","version":1},"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:be3e44fabd3cb922da50f0e79403335d14d4431a9c8cb3718541a269edaca0fe","target":"record","created_at":"2026-05-18T00:46:31Z","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":"2490a6c2baada51c28e0696a0e49333fa6daefe218ddc53f2934dd7db83f2db9","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-25T09:42:46Z","title_canon_sha256":"4a4e2fa1d7ae9ee7f821cf20f23ccc74b61d2cec25ac19cc5d55c2b4e2b85c22"},"schema_version":"1.0","source":{"id":"1602.07863","kind":"arxiv","version":1}},"canonical_sha256":"c70aa1b1695d8a8ae2ad44efcdecbf1a7581956f1d95552b09d2f348e6253aa7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c70aa1b1695d8a8ae2ad44efcdecbf1a7581956f1d95552b09d2f348e6253aa7","first_computed_at":"2026-05-18T00:46:31.985746Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:46:31.985746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pAdoULXPzD6LLtRCJFOWIUQVB1dh8l3uurjGrMulAhO6rQgApFVqHt7JyM5ERBk794k+k/iiz3/QxVNEstYoBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:46:31.986521Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.07863","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:be3e44fabd3cb922da50f0e79403335d14d4431a9c8cb3718541a269edaca0fe","sha256:abc47b13aed977bf8a702d946417933319b47bd42f1d950576f1f0344ede8b2b"],"state_sha256":"593d6e78e80073d1d0be63436bb948fb4c74538ab452c57cb1f8d26db75955a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VyoItSyvxYMepEAJkVRr2FGMwtTYvJ423pw2JjSG1nQBpDCl5tVTQ4/lY2esu+NhuQizaQ5a42uxOI3bjcsWAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:00:51.091441Z","bundle_sha256":"57bc8769ef80d6bd9bb1b0d0d2d9d9184be28d6e8c6c4d9690d92681110cab9e"}}