{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NGVPTXYCUTH6LCBFGIE74HRWIO","short_pith_number":"pith:NGVPTXYC","schema_version":"1.0","canonical_sha256":"69aaf9df02a4cfe588253209fe1e3643bdc7871319295e67c188ec28c821e4da","source":{"kind":"arxiv","id":"1806.02415","version":1},"attestation_state":"computed","paper":{"title":"Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Cheol Young Park, Kathryn Blackmond Laskey, Paulo C. G. Costa, Shou Matsumoto","submitted_at":"2018-06-06T20:38:27Z","abstract_excerpt":"Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be perfo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1806.02415","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-06-06T20:38:27Z","cross_cats_sorted":[],"title_canon_sha256":"2cc0cee03a7acb0ea750e5af17b7affdcc66d977963011396b271aa055f228e9","abstract_canon_sha256":"96cc65b56e6ae13b8cc8a78a41d48d9df9dd56f2277eb4d335dde74b01b43672"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:53.072076Z","signature_b64":"wtt9KCoZ/YbZGGsCaAjpUdmFlSVbTVEn56L99MPU5c8EFSxfga1GSlcUZw/60knnQ+55D+UG7BOVfaet50a/CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69aaf9df02a4cfe588253209fe1e3643bdc7871319295e67c188ec28c821e4da","last_reissued_at":"2026-05-17T23:45:53.071655Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:53.071655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Cheol Young Park, Kathryn Blackmond Laskey, Paulo C. G. Costa, Shou Matsumoto","submitted_at":"2018-06-06T20:38:27Z","abstract_excerpt":"Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be perfo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.02415","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1806.02415","created_at":"2026-05-17T23:45:53.071736+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.02415v1","created_at":"2026-05-17T23:45:53.071736+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.02415","created_at":"2026-05-17T23:45:53.071736+00:00"},{"alias_kind":"pith_short_12","alias_value":"NGVPTXYCUTH6","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NGVPTXYCUTH6LCBF","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NGVPTXYC","created_at":"2026-05-18T12:32:40.477152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO","json":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO.json","graph_json":"https://pith.science/api/pith-number/NGVPTXYCUTH6LCBFGIE74HRWIO/graph.json","events_json":"https://pith.science/api/pith-number/NGVPTXYCUTH6LCBFGIE74HRWIO/events.json","paper":"https://pith.science/paper/NGVPTXYC"},"agent_actions":{"view_html":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO","download_json":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO.json","view_paper":"https://pith.science/paper/NGVPTXYC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.02415&json=true","fetch_graph":"https://pith.science/api/pith-number/NGVPTXYCUTH6LCBFGIE74HRWIO/graph.json","fetch_events":"https://pith.science/api/pith-number/NGVPTXYCUTH6LCBFGIE74HRWIO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO/action/storage_attestation","attest_author":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO/action/author_attestation","sign_citation":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO/action/citation_signature","submit_replication":"https://pith.science/pith/NGVPTXYCUTH6LCBFGIE74HRWIO/action/replication_record"}},"created_at":"2026-05-17T23:45:53.071736+00:00","updated_at":"2026-05-17T23:45:53.071736+00:00"}