{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BGYZCJXJYR6BM6CBEVPP5DJRJ6","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":"7f8966d8519e4622af81343d29fe0028c1bd406889c5607c1b7931c03cc5313b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-29T10:30:13Z","title_canon_sha256":"efec16497c62b22e6c8c8c054dae9cc6e5bb1b691682747384cf020d5cc0b874"},"schema_version":"1.0","source":{"id":"2505.23320","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.23320","created_at":"2026-07-05T11:11:59Z"},{"alias_kind":"arxiv_version","alias_value":"2505.23320v1","created_at":"2026-07-05T11:11:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.23320","created_at":"2026-07-05T11:11:59Z"},{"alias_kind":"pith_short_12","alias_value":"BGYZCJXJYR6B","created_at":"2026-07-05T11:11:59Z"},{"alias_kind":"pith_short_16","alias_value":"BGYZCJXJYR6BM6CB","created_at":"2026-07-05T11:11:59Z"},{"alias_kind":"pith_short_8","alias_value":"BGYZCJXJ","created_at":"2026-07-05T11:11:59Z"}],"graph_snapshots":[{"event_id":"sha256:8a4ad8e87e6a9c854e8e65fc2e4121b3ff8dbebe986c3d3ecccf2bd5ba9fc23f","target":"graph","created_at":"2026-07-05T11:11:59Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.23320/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian network classifiers (BNCs) possess a number of properties desirable for a modern classifier: They are easily interpretable, highly scalable, and offer adaptable complexity. However, traditional methods for learning BNCs have historically underperformed when compared to leading classification methods such as random forests. Recent parameter smoothing techniques using hierarchical Dirichlet processes (HDPs) have enabled BNCs to achieve performance competitive with random forests on categorical data, but these techniques are relatively inflexible, and require a complicated, specialized s","authors_text":"Connor Cooper, Daniel F. Schmidt, Geoffrey I. Webb","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-29T10:30:13Z","title":"Efficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.23320","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":"sha256:395d04cf93e47ffe02653185ce4c5ffba08f092e6c18b48b3f8043bb59ed60f0","target":"record","created_at":"2026-07-05T11:11:59Z","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":"7f8966d8519e4622af81343d29fe0028c1bd406889c5607c1b7931c03cc5313b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-29T10:30:13Z","title_canon_sha256":"efec16497c62b22e6c8c8c054dae9cc6e5bb1b691682747384cf020d5cc0b874"},"schema_version":"1.0","source":{"id":"2505.23320","kind":"arxiv","version":1}},"canonical_sha256":"09b19126e9c47c167841255efe8d314f99e57a69776ed0e2f7440da6be84c78e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"09b19126e9c47c167841255efe8d314f99e57a69776ed0e2f7440da6be84c78e","first_computed_at":"2026-07-05T11:11:59.549983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:11:59.549983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mYZhEfALu69il/o3zP1LB83kZG2ZfuBrSx/EZHPxUzAjVs63Cp0XbX+6NDv8Oeg2B3jWJ3zbXh35fO6Pgu5ZCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:11:59.550483Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.23320","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:395d04cf93e47ffe02653185ce4c5ffba08f092e6c18b48b3f8043bb59ed60f0","sha256:8a4ad8e87e6a9c854e8e65fc2e4121b3ff8dbebe986c3d3ecccf2bd5ba9fc23f"],"state_sha256":"746a0998271272027fe82d156f362979ccef8d84c788a4632c4a1189e63f165e"}