{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:SJ7KQYOKU2SKIL7REN5NKQDVH2","short_pith_number":"pith:SJ7KQYOK","canonical_record":{"source":{"id":"1401.3886","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-01-16T05:15:08Z","cross_cats_sorted":[],"title_canon_sha256":"85f1dd3f1ba4f71e0d2d75a69f5b183ba015816c36d1bdcd942ec094db3da473","abstract_canon_sha256":"9621f2176d5f2c1cfc1ec16daf2828371a8317d623218e83b921905653a24fc1"},"schema_version":"1.0"},"canonical_sha256":"927ea861caa6a4a42ff1237ad540753eb33693a27c18291b8e49dd78e5bb3870","source":{"kind":"arxiv","id":"1401.3886","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1401.3886","created_at":"2026-05-18T03:02:06Z"},{"alias_kind":"arxiv_version","alias_value":"1401.3886v1","created_at":"2026-05-18T03:02:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.3886","created_at":"2026-05-18T03:02:06Z"},{"alias_kind":"pith_short_12","alias_value":"SJ7KQYOKU2SK","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_16","alias_value":"SJ7KQYOKU2SKIL7R","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_8","alias_value":"SJ7KQYOK","created_at":"2026-05-18T12:28:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:SJ7KQYOKU2SKIL7REN5NKQDVH2","target":"record","payload":{"canonical_record":{"source":{"id":"1401.3886","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-01-16T05:15:08Z","cross_cats_sorted":[],"title_canon_sha256":"85f1dd3f1ba4f71e0d2d75a69f5b183ba015816c36d1bdcd942ec094db3da473","abstract_canon_sha256":"9621f2176d5f2c1cfc1ec16daf2828371a8317d623218e83b921905653a24fc1"},"schema_version":"1.0"},"canonical_sha256":"927ea861caa6a4a42ff1237ad540753eb33693a27c18291b8e49dd78e5bb3870","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:02:06.249100Z","signature_b64":"WsXtHJPb9pu2Y6Jn4kzce935xSeLHZq4kqeVdRfZLqFDUelXCfDH5hEystAerOiP4IiYWUjYgZUW60dj+pkgDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"927ea861caa6a4a42ff1237ad540753eb33693a27c18291b8e49dd78e5bb3870","last_reissued_at":"2026-05-18T03:02:06.248547Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:02:06.248547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1401.3886","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":"2026-05-18T03:02:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1em9ngws+dfIq81O9+9Sh/MQ7uPnwUA19sZBYqN0XavC8A9L1fjIeyS6nkzZ4aK7dgWEtz6cHLuL1cKnfN54Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T17:18:53.329489Z"},"content_sha256":"db4831efae59138385f42bc2761005b6279b0c0d86156763d94db4feb1432176","schema_version":"1.0","event_id":"sha256:db4831efae59138385f42bc2761005b6279b0c0d86156763d94db4feb1432176"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:SJ7KQYOKU2SKIL7REN5NKQDVH2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Pascal Poupart, Peter van Beek, Wei Li","submitted_at":"2014-01-16T05:15:08Z","abstract_excerpt":"Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference.  In this paper, we present techniques for improving this approach for Bayesian networks with noisy-OR and noisy-MAX relations---two relations that are widely used in practice as they can dramatically reduce the number of probabilities one needs to specify. In particular, we present two SAT encodings for noisy-OR and two encodings for noisy-MAX that exploit the structure or semanti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.3886","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":"2026-05-18T03:02:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QcVpa38Jze9KpYfUYamhTiwMPQZ2w9R//JaGXuX/OFit4Ic96j5AfbWUFkYEFJtOM0qF3QBQOHsgtyLm7z5AAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T17:18:53.330041Z"},"content_sha256":"0b7555b137fb1f0609606d9699994cfff977fe2202d555ea90ac8df96c3b1451","schema_version":"1.0","event_id":"sha256:0b7555b137fb1f0609606d9699994cfff977fe2202d555ea90ac8df96c3b1451"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SJ7KQYOKU2SKIL7REN5NKQDVH2/bundle.json","state_url":"https://pith.science/pith/SJ7KQYOKU2SKIL7REN5NKQDVH2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SJ7KQYOKU2SKIL7REN5NKQDVH2/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":"2026-06-04T17:18:53Z","links":{"resolver":"https://pith.science/pith/SJ7KQYOKU2SKIL7REN5NKQDVH2","bundle":"https://pith.science/pith/SJ7KQYOKU2SKIL7REN5NKQDVH2/bundle.json","state":"https://pith.science/pith/SJ7KQYOKU2SKIL7REN5NKQDVH2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SJ7KQYOKU2SKIL7REN5NKQDVH2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:SJ7KQYOKU2SKIL7REN5NKQDVH2","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":"9621f2176d5f2c1cfc1ec16daf2828371a8317d623218e83b921905653a24fc1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-01-16T05:15:08Z","title_canon_sha256":"85f1dd3f1ba4f71e0d2d75a69f5b183ba015816c36d1bdcd942ec094db3da473"},"schema_version":"1.0","source":{"id":"1401.3886","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1401.3886","created_at":"2026-05-18T03:02:06Z"},{"alias_kind":"arxiv_version","alias_value":"1401.3886v1","created_at":"2026-05-18T03:02:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.3886","created_at":"2026-05-18T03:02:06Z"},{"alias_kind":"pith_short_12","alias_value":"SJ7KQYOKU2SK","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_16","alias_value":"SJ7KQYOKU2SKIL7R","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_8","alias_value":"SJ7KQYOK","created_at":"2026-05-18T12:28:49Z"}],"graph_snapshots":[{"event_id":"sha256:0b7555b137fb1f0609606d9699994cfff977fe2202d555ea90ac8df96c3b1451","target":"graph","created_at":"2026-05-18T03:02:06Z","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":"Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference.  In this paper, we present techniques for improving this approach for Bayesian networks with noisy-OR and noisy-MAX relations---two relations that are widely used in practice as they can dramatically reduce the number of probabilities one needs to specify. In particular, we present two SAT encodings for noisy-OR and two encodings for noisy-MAX that exploit the structure or semanti","authors_text":"Pascal Poupart, Peter van Beek, Wei Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-01-16T05:15:08Z","title":"Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.3886","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:db4831efae59138385f42bc2761005b6279b0c0d86156763d94db4feb1432176","target":"record","created_at":"2026-05-18T03:02:06Z","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":"9621f2176d5f2c1cfc1ec16daf2828371a8317d623218e83b921905653a24fc1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-01-16T05:15:08Z","title_canon_sha256":"85f1dd3f1ba4f71e0d2d75a69f5b183ba015816c36d1bdcd942ec094db3da473"},"schema_version":"1.0","source":{"id":"1401.3886","kind":"arxiv","version":1}},"canonical_sha256":"927ea861caa6a4a42ff1237ad540753eb33693a27c18291b8e49dd78e5bb3870","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"927ea861caa6a4a42ff1237ad540753eb33693a27c18291b8e49dd78e5bb3870","first_computed_at":"2026-05-18T03:02:06.248547Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:02:06.248547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WsXtHJPb9pu2Y6Jn4kzce935xSeLHZq4kqeVdRfZLqFDUelXCfDH5hEystAerOiP4IiYWUjYgZUW60dj+pkgDg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:02:06.249100Z","signed_message":"canonical_sha256_bytes"},"source_id":"1401.3886","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db4831efae59138385f42bc2761005b6279b0c0d86156763d94db4feb1432176","sha256:0b7555b137fb1f0609606d9699994cfff977fe2202d555ea90ac8df96c3b1451"],"state_sha256":"b857ef18c8dc92ad43b5639b9aa707b215cbc3b2601adf5848746860f5bbfe3a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jW7gZzUZ2coX5L99ec0e5ZSw4luI3QrYZCsWmtUT1+MNIZivLsrbCSLpaiQy/u/S/g9DLXXNUj/XphVEdJK1BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T17:18:53.332993Z","bundle_sha256":"dff778ee973fb31ff5ef757b225c05c98f531207af81f4f745564271217a9260"}}