{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:KZGQE2GCDJN5CZG3QWGACSED5L","short_pith_number":"pith:KZGQE2GC","canonical_record":{"source":{"id":"1207.1387","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-07-04T16:13:39Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"6b1877f266159093f3ee6388a4fc6cd881a78e55bbcf79d8909f5855b7dc4689","abstract_canon_sha256":"760af8ff811868456445d9f32cb71f0047c56c68100879c9bab175162807f9b6"},"schema_version":"1.0"},"canonical_sha256":"564d0268c21a5bd164db858c014883ead28b47e8b5045b1f0943cf61e200bc10","source":{"kind":"arxiv","id":"1207.1387","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1207.1387","created_at":"2026-05-18T03:51:37Z"},{"alias_kind":"arxiv_version","alias_value":"1207.1387v1","created_at":"2026-05-18T03:51:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.1387","created_at":"2026-05-18T03:51:37Z"},{"alias_kind":"pith_short_12","alias_value":"KZGQE2GCDJN5","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"KZGQE2GCDJN5CZG3","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"KZGQE2GC","created_at":"2026-05-18T12:27:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:KZGQE2GCDJN5CZG3QWGACSED5L","target":"record","payload":{"canonical_record":{"source":{"id":"1207.1387","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-07-04T16:13:39Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"6b1877f266159093f3ee6388a4fc6cd881a78e55bbcf79d8909f5855b7dc4689","abstract_canon_sha256":"760af8ff811868456445d9f32cb71f0047c56c68100879c9bab175162807f9b6"},"schema_version":"1.0"},"canonical_sha256":"564d0268c21a5bd164db858c014883ead28b47e8b5045b1f0943cf61e200bc10","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:51:37.439878Z","signature_b64":"Jv3SchDA6aFsjQB1AW8d63zQ8pXIWRw/AMNwT57Ofo5Q3FXdJooZqANIv4ls9Tz6J8P8veSEmA9qCI/+UWfFDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"564d0268c21a5bd164db858c014883ead28b47e8b5045b1f0943cf61e200bc10","last_reissued_at":"2026-05-18T03:51:37.439063Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:51:37.439063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1207.1387","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":"2026-05-18T03:51:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VrYSuhNt02DTOe2Rzu9grH9t5i5wXypcZDp3oEYh1hdznBL53nznJld8sCrxQtIMan4ypf77C1UeaXFdnNn1DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:28:41.304956Z"},"content_sha256":"c46a07b40a929b282a6a4fd86eb499c28e5f674fbda141ebc00a380ba14e6a7e","schema_version":"1.0","event_id":"sha256:c46a07b40a929b282a6a4fd86eb499c28e5f674fbda141ebc00a380ba14e6a7e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:KZGQE2GCDJN5CZG3QWGACSED5L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Ad Feelders, Linda C. van der Gaag","submitted_at":"2012-07-04T16:13:39Z","abstract_excerpt":"We present a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables. Our method accommodates not just the standard signs, but provides for context-specific signs as well. We show how the various signs translate into order constraints on the network parameters and how isotonic regression can be used to compute order-constrained estimates from the available data. Our experimental results show that taking prior knowledge about the signs of influences into account leads to an improved fit of the true distribution, especially wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.1387","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":"2026-05-18T03:51:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"juYa6Z6q0SgMxXmobsxb932GYrnXyaEzclddtkytgTW3wcE6SLkyhHLCYZupRwlo+k9xM6To5It/DgF7nZgVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:28:41.305292Z"},"content_sha256":"f33ac30977c634f900b08fefd9382496242328f6ad844f243b0fb653caafee6b","schema_version":"1.0","event_id":"sha256:f33ac30977c634f900b08fefd9382496242328f6ad844f243b0fb653caafee6b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KZGQE2GCDJN5CZG3QWGACSED5L/bundle.json","state_url":"https://pith.science/pith/KZGQE2GCDJN5CZG3QWGACSED5L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KZGQE2GCDJN5CZG3QWGACSED5L/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":"2026-06-01T21:28:41Z","links":{"resolver":"https://pith.science/pith/KZGQE2GCDJN5CZG3QWGACSED5L","bundle":"https://pith.science/pith/KZGQE2GCDJN5CZG3QWGACSED5L/bundle.json","state":"https://pith.science/pith/KZGQE2GCDJN5CZG3QWGACSED5L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KZGQE2GCDJN5CZG3QWGACSED5L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:KZGQE2GCDJN5CZG3QWGACSED5L","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":"760af8ff811868456445d9f32cb71f0047c56c68100879c9bab175162807f9b6","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-07-04T16:13:39Z","title_canon_sha256":"6b1877f266159093f3ee6388a4fc6cd881a78e55bbcf79d8909f5855b7dc4689"},"schema_version":"1.0","source":{"id":"1207.1387","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1207.1387","created_at":"2026-05-18T03:51:37Z"},{"alias_kind":"arxiv_version","alias_value":"1207.1387v1","created_at":"2026-05-18T03:51:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.1387","created_at":"2026-05-18T03:51:37Z"},{"alias_kind":"pith_short_12","alias_value":"KZGQE2GCDJN5","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"KZGQE2GCDJN5CZG3","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"KZGQE2GC","created_at":"2026-05-18T12:27:11Z"}],"graph_snapshots":[{"event_id":"sha256:f33ac30977c634f900b08fefd9382496242328f6ad844f243b0fb653caafee6b","target":"graph","created_at":"2026-05-18T03:51:37Z","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 a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables. Our method accommodates not just the standard signs, but provides for context-specific signs as well. We show how the various signs translate into order constraints on the network parameters and how isotonic regression can be used to compute order-constrained estimates from the available data. Our experimental results show that taking prior knowledge about the signs of influences into account leads to an improved fit of the true distribution, especially wh","authors_text":"Ad Feelders, Linda C. van der Gaag","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-07-04T16:13:39Z","title":"Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.1387","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:c46a07b40a929b282a6a4fd86eb499c28e5f674fbda141ebc00a380ba14e6a7e","target":"record","created_at":"2026-05-18T03:51:37Z","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":"760af8ff811868456445d9f32cb71f0047c56c68100879c9bab175162807f9b6","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-07-04T16:13:39Z","title_canon_sha256":"6b1877f266159093f3ee6388a4fc6cd881a78e55bbcf79d8909f5855b7dc4689"},"schema_version":"1.0","source":{"id":"1207.1387","kind":"arxiv","version":1}},"canonical_sha256":"564d0268c21a5bd164db858c014883ead28b47e8b5045b1f0943cf61e200bc10","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"564d0268c21a5bd164db858c014883ead28b47e8b5045b1f0943cf61e200bc10","first_computed_at":"2026-05-18T03:51:37.439063Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:51:37.439063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Jv3SchDA6aFsjQB1AW8d63zQ8pXIWRw/AMNwT57Ofo5Q3FXdJooZqANIv4ls9Tz6J8P8veSEmA9qCI/+UWfFDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:51:37.439878Z","signed_message":"canonical_sha256_bytes"},"source_id":"1207.1387","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c46a07b40a929b282a6a4fd86eb499c28e5f674fbda141ebc00a380ba14e6a7e","sha256:f33ac30977c634f900b08fefd9382496242328f6ad844f243b0fb653caafee6b"],"state_sha256":"0deddc45bfcd892860070425f6f59e31561950b3cd54a0898d584817eb18e7f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2MsP6+z8wWUtucC+sC1vbmHKRfFhmlDHvEkbJcmqqmC/DTvWBF/rp/NxQSVNS6cpIH22qSzd7y+wXjJtajw7CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T21:28:41.307140Z","bundle_sha256":"536a1680a076e3ff51ceb056d065eae48d964a2c695996d88b72e89863cb2d33"}}