{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:N33A3TR57LUUUJJOMALDCOXNMV","short_pith_number":"pith:N33A3TR5","canonical_record":{"source":{"id":"1704.03942","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-04-12T22:01:09Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"d7e380a72dd11350e11e5e6d149c7e39e979cf0ef4c81464f532a708eef3d417","abstract_canon_sha256":"ea56ce90d2ec6ba598688be4a2b2cfa0c13aba0f4fe9f1f6960fd13da1d59800"},"schema_version":"1.0"},"canonical_sha256":"6ef60dce3dfae94a252e6016313aed655ce156039ee76a2ded3e5ed24565cd5c","source":{"kind":"arxiv","id":"1704.03942","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.03942","created_at":"2026-05-18T00:46:25Z"},{"alias_kind":"arxiv_version","alias_value":"1704.03942v1","created_at":"2026-05-18T00:46:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.03942","created_at":"2026-05-18T00:46:25Z"},{"alias_kind":"pith_short_12","alias_value":"N33A3TR57LUU","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"N33A3TR57LUUUJJO","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"N33A3TR5","created_at":"2026-05-18T12:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:N33A3TR57LUUUJJOMALDCOXNMV","target":"record","payload":{"canonical_record":{"source":{"id":"1704.03942","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-04-12T22:01:09Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"d7e380a72dd11350e11e5e6d149c7e39e979cf0ef4c81464f532a708eef3d417","abstract_canon_sha256":"ea56ce90d2ec6ba598688be4a2b2cfa0c13aba0f4fe9f1f6960fd13da1d59800"},"schema_version":"1.0"},"canonical_sha256":"6ef60dce3dfae94a252e6016313aed655ce156039ee76a2ded3e5ed24565cd5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:25.831254Z","signature_b64":"bhdXtPwhJS6kb95/Fp7dw3VO22IXGftMdVjaekutY/xM9Vv9M+wEt90i+2A7VMXL9JnVQmSg/ayCiheTaChmDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ef60dce3dfae94a252e6016313aed655ce156039ee76a2ded3e5ed24565cd5c","last_reissued_at":"2026-05-18T00:46:25.830685Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:25.830685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.03942","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":"2026-05-18T00:46:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SQkQAGm7mhboqkr46AmLo4yvBa9ptiaHItHcYArn7ndO3+bGOBO6zvZ+xrkoYUr9w5qyPmRwnbS+QCDN+PqsDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T08:40:46.084292Z"},"content_sha256":"459e00adac8e2d4b069ae4fb8d3113ef674711eac903cbe9d93323f822966a19","schema_version":"1.0","event_id":"sha256:459e00adac8e2d4b069ae4fb8d3113ef674711eac903cbe9d93323f822966a19"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:N33A3TR57LUUUJJOMALDCOXNMV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Uniform Priors in Bayesian Network Structure Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.ML","authors_text":"Marco Scutari","submitted_at":"2017-04-12T22:01:09Z","abstract_excerpt":"Bayesian network structure learning is often performed in a Bayesian setting, evaluating candidate structures using their posterior probabilities for a given data set. Score-based algorithms then use those posterior probabilities as an objective function and return the maximum a posteriori network as the learned model. For discrete Bayesian networks, the canonical choice for a posterior score is the Bayesian Dirichlet equivalent uniform (BDeu) marginal likelihood with a uniform (U) graph prior, which assumes a uniform prior both on the network structures and on the parameters of the networks. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.03942","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":"2026-05-18T00:46:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RccyLfh/Gd96GrIJLsnYwNqjnI1X5GGmLSbMKH5W3I0HNldCTC/Q2TGNdJGc45JXFCAjJBd5cdcFMvR5ZwFMDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T08:40:46.084694Z"},"content_sha256":"d465dc3bf024f389c1e44e2858331cce9bcf9b70d742bbda4e5646db6b58a87a","schema_version":"1.0","event_id":"sha256:d465dc3bf024f389c1e44e2858331cce9bcf9b70d742bbda4e5646db6b58a87a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N33A3TR57LUUUJJOMALDCOXNMV/bundle.json","state_url":"https://pith.science/pith/N33A3TR57LUUUJJOMALDCOXNMV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N33A3TR57LUUUJJOMALDCOXNMV/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":"2026-06-11T08:40:46Z","links":{"resolver":"https://pith.science/pith/N33A3TR57LUUUJJOMALDCOXNMV","bundle":"https://pith.science/pith/N33A3TR57LUUUJJOMALDCOXNMV/bundle.json","state":"https://pith.science/pith/N33A3TR57LUUUJJOMALDCOXNMV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N33A3TR57LUUUJJOMALDCOXNMV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:N33A3TR57LUUUJJOMALDCOXNMV","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":"ea56ce90d2ec6ba598688be4a2b2cfa0c13aba0f4fe9f1f6960fd13da1d59800","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-04-12T22:01:09Z","title_canon_sha256":"d7e380a72dd11350e11e5e6d149c7e39e979cf0ef4c81464f532a708eef3d417"},"schema_version":"1.0","source":{"id":"1704.03942","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.03942","created_at":"2026-05-18T00:46:25Z"},{"alias_kind":"arxiv_version","alias_value":"1704.03942v1","created_at":"2026-05-18T00:46:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.03942","created_at":"2026-05-18T00:46:25Z"},{"alias_kind":"pith_short_12","alias_value":"N33A3TR57LUU","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"N33A3TR57LUUUJJO","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"N33A3TR5","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:d465dc3bf024f389c1e44e2858331cce9bcf9b70d742bbda4e5646db6b58a87a","target":"graph","created_at":"2026-05-18T00:46:25Z","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":"Bayesian network structure learning is often performed in a Bayesian setting, evaluating candidate structures using their posterior probabilities for a given data set. Score-based algorithms then use those posterior probabilities as an objective function and return the maximum a posteriori network as the learned model. For discrete Bayesian networks, the canonical choice for a posterior score is the Bayesian Dirichlet equivalent uniform (BDeu) marginal likelihood with a uniform (U) graph prior, which assumes a uniform prior both on the network structures and on the parameters of the networks. ","authors_text":"Marco Scutari","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-04-12T22:01:09Z","title":"Beyond Uniform Priors in Bayesian Network Structure Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.03942","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":"sha256:459e00adac8e2d4b069ae4fb8d3113ef674711eac903cbe9d93323f822966a19","target":"record","created_at":"2026-05-18T00:46:25Z","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":"ea56ce90d2ec6ba598688be4a2b2cfa0c13aba0f4fe9f1f6960fd13da1d59800","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-04-12T22:01:09Z","title_canon_sha256":"d7e380a72dd11350e11e5e6d149c7e39e979cf0ef4c81464f532a708eef3d417"},"schema_version":"1.0","source":{"id":"1704.03942","kind":"arxiv","version":1}},"canonical_sha256":"6ef60dce3dfae94a252e6016313aed655ce156039ee76a2ded3e5ed24565cd5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6ef60dce3dfae94a252e6016313aed655ce156039ee76a2ded3e5ed24565cd5c","first_computed_at":"2026-05-18T00:46:25.830685Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:46:25.830685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bhdXtPwhJS6kb95/Fp7dw3VO22IXGftMdVjaekutY/xM9Vv9M+wEt90i+2A7VMXL9JnVQmSg/ayCiheTaChmDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:46:25.831254Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.03942","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:459e00adac8e2d4b069ae4fb8d3113ef674711eac903cbe9d93323f822966a19","sha256:d465dc3bf024f389c1e44e2858331cce9bcf9b70d742bbda4e5646db6b58a87a"],"state_sha256":"782e7442acb8baad9a2014fd66d4cc82b304b347463dc51d5372ff71bf85dbea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cuuhmoXKmhOoezsUWK0usdn02YQDM5U6+8clKzu4gPbYmg17vNwH8cmL5N9JE34wyr+InKhjYd0gb5Gp2kezAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T08:40:46.086870Z","bundle_sha256":"cd0cb36c604e4c557640b065fbbe33d95b34929df3bdb459b43180ad86266d73"}}