{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:XE6ZQESGZTOKSH72RRT7SYCZYV","short_pith_number":"pith:XE6ZQESG","canonical_record":{"source":{"id":"1105.5462","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2011-05-27T01:53:36Z","cross_cats_sorted":[],"title_canon_sha256":"08db3a36e7956a1bcc0737109fb65fe315f40499ed781679930c55cf43277339","abstract_canon_sha256":"e9f7b8d134f34d5a7ab063fd33d4df404b75c3d0bbe151f82898f1894d06cfc2"},"schema_version":"1.0"},"canonical_sha256":"b93d981246ccdca91ffa8c67f96059c55f303e0273b023a9fde8249ef6222fdb","source":{"kind":"arxiv","id":"1105.5462","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1105.5462","created_at":"2026-05-18T04:21:11Z"},{"alias_kind":"arxiv_version","alias_value":"1105.5462v1","created_at":"2026-05-18T04:21:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1105.5462","created_at":"2026-05-18T04:21:11Z"},{"alias_kind":"pith_short_12","alias_value":"XE6ZQESGZTOK","created_at":"2026-05-18T12:26:44Z"},{"alias_kind":"pith_short_16","alias_value":"XE6ZQESGZTOKSH72","created_at":"2026-05-18T12:26:44Z"},{"alias_kind":"pith_short_8","alias_value":"XE6ZQESG","created_at":"2026-05-18T12:26:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:XE6ZQESGZTOKSH72RRT7SYCZYV","target":"record","payload":{"canonical_record":{"source":{"id":"1105.5462","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2011-05-27T01:53:36Z","cross_cats_sorted":[],"title_canon_sha256":"08db3a36e7956a1bcc0737109fb65fe315f40499ed781679930c55cf43277339","abstract_canon_sha256":"e9f7b8d134f34d5a7ab063fd33d4df404b75c3d0bbe151f82898f1894d06cfc2"},"schema_version":"1.0"},"canonical_sha256":"b93d981246ccdca91ffa8c67f96059c55f303e0273b023a9fde8249ef6222fdb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:21:11.658199Z","signature_b64":"ZXkKfQX5f/ZAVX4XJjPUpiQhEHrFzaAZH750dvi2AIJfCqlcUtYt+Ryex6LCZe/jtEyPSt+DB+cDScRe5PyrCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b93d981246ccdca91ffa8c67f96059c55f303e0273b023a9fde8249ef6222fdb","last_reissued_at":"2026-05-18T04:21:11.657632Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:21:11.657632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1105.5462","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":"2026-05-18T04:21:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EzFq2ypKqziM13xZkiGZ8pB/pa6an7LgzOtb9ZL9UzANQ0Tlhxswc1bO1uDHDMow4RGL1f0kKMatZy4zKoXhBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:01:45.394594Z"},"content_sha256":"172ea75031ae32d1d2007ea99288c853997805aa2111ff40203793c426c8f2d9","schema_version":"1.0","event_id":"sha256:172ea75031ae32d1d2007ea99288c853997805aa2111ff40203793c426c8f2d9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:XE6ZQESGZTOKSH72RRT7SYCZYV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational Probabilistic Inference and the QMR-DT Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"M. I. Jordan, T. S. Jaakkola","submitted_at":"2011-05-27T01:53:36Z","abstract_excerpt":"We describe a variational approximation method for efficient    inference in large-scale probabilistic models.  Variational methods    are deterministic procedures that provide approximations to marginal    and conditional probabilities of interest.  They provide alternatives    to approximate inference methods based on stochastic sampling or    search.  We describe a variational approach to the problem of    diagnostic inference in the `Quick Medical Reference' (QMR) network.    The QMR network is a large-scale probabilistic graphical model built    on statistical and expert knowledge.  Exact"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1105.5462","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":"2026-05-18T04:21:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZkLXQ2ECBCipmWTtAz/MZHD+ZbLdWX0zHLWdC63Og7PWCxJtuiFQU8DfbyQXcEbPi8Pwfu6BMgf8lAAJyF/0CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:01:45.395109Z"},"content_sha256":"baed2e9254fcae49d332766b9a1b286e660f853671ee09fc59d0214919694eab","schema_version":"1.0","event_id":"sha256:baed2e9254fcae49d332766b9a1b286e660f853671ee09fc59d0214919694eab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XE6ZQESGZTOKSH72RRT7SYCZYV/bundle.json","state_url":"https://pith.science/pith/XE6ZQESGZTOKSH72RRT7SYCZYV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XE6ZQESGZTOKSH72RRT7SYCZYV/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":"2026-05-26T11:01:45Z","links":{"resolver":"https://pith.science/pith/XE6ZQESGZTOKSH72RRT7SYCZYV","bundle":"https://pith.science/pith/XE6ZQESGZTOKSH72RRT7SYCZYV/bundle.json","state":"https://pith.science/pith/XE6ZQESGZTOKSH72RRT7SYCZYV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XE6ZQESGZTOKSH72RRT7SYCZYV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:XE6ZQESGZTOKSH72RRT7SYCZYV","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":"e9f7b8d134f34d5a7ab063fd33d4df404b75c3d0bbe151f82898f1894d06cfc2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2011-05-27T01:53:36Z","title_canon_sha256":"08db3a36e7956a1bcc0737109fb65fe315f40499ed781679930c55cf43277339"},"schema_version":"1.0","source":{"id":"1105.5462","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1105.5462","created_at":"2026-05-18T04:21:11Z"},{"alias_kind":"arxiv_version","alias_value":"1105.5462v1","created_at":"2026-05-18T04:21:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1105.5462","created_at":"2026-05-18T04:21:11Z"},{"alias_kind":"pith_short_12","alias_value":"XE6ZQESGZTOK","created_at":"2026-05-18T12:26:44Z"},{"alias_kind":"pith_short_16","alias_value":"XE6ZQESGZTOKSH72","created_at":"2026-05-18T12:26:44Z"},{"alias_kind":"pith_short_8","alias_value":"XE6ZQESG","created_at":"2026-05-18T12:26:44Z"}],"graph_snapshots":[{"event_id":"sha256:baed2e9254fcae49d332766b9a1b286e660f853671ee09fc59d0214919694eab","target":"graph","created_at":"2026-05-18T04:21:11Z","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 describe a variational approximation method for efficient    inference in large-scale probabilistic models.  Variational methods    are deterministic procedures that provide approximations to marginal    and conditional probabilities of interest.  They provide alternatives    to approximate inference methods based on stochastic sampling or    search.  We describe a variational approach to the problem of    diagnostic inference in the `Quick Medical Reference' (QMR) network.    The QMR network is a large-scale probabilistic graphical model built    on statistical and expert knowledge.  Exact","authors_text":"M. I. Jordan, T. S. Jaakkola","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2011-05-27T01:53:36Z","title":"Variational Probabilistic Inference and the QMR-DT Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1105.5462","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":"sha256:172ea75031ae32d1d2007ea99288c853997805aa2111ff40203793c426c8f2d9","target":"record","created_at":"2026-05-18T04:21:11Z","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":"e9f7b8d134f34d5a7ab063fd33d4df404b75c3d0bbe151f82898f1894d06cfc2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2011-05-27T01:53:36Z","title_canon_sha256":"08db3a36e7956a1bcc0737109fb65fe315f40499ed781679930c55cf43277339"},"schema_version":"1.0","source":{"id":"1105.5462","kind":"arxiv","version":1}},"canonical_sha256":"b93d981246ccdca91ffa8c67f96059c55f303e0273b023a9fde8249ef6222fdb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b93d981246ccdca91ffa8c67f96059c55f303e0273b023a9fde8249ef6222fdb","first_computed_at":"2026-05-18T04:21:11.657632Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:21:11.657632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZXkKfQX5f/ZAVX4XJjPUpiQhEHrFzaAZH750dvi2AIJfCqlcUtYt+Ryex6LCZe/jtEyPSt+DB+cDScRe5PyrCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T04:21:11.658199Z","signed_message":"canonical_sha256_bytes"},"source_id":"1105.5462","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:172ea75031ae32d1d2007ea99288c853997805aa2111ff40203793c426c8f2d9","sha256:baed2e9254fcae49d332766b9a1b286e660f853671ee09fc59d0214919694eab"],"state_sha256":"0086433719bdbddefdba3e5b7a51d50d4a445ebc064cdd9e4ce851e2f981959c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2+9gvW6QeiZv0AURKKyEMThdbzZs55Arltc/xnLJHBrj/icvf1mKu3//khrfPP77Xgpyk+mqpxXA4qBVItF1BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:01:45.397952Z","bundle_sha256":"d01de2d8b901d7bd1cd59394b04409606a7f6898f9eabff6468f7ea65bef402c"}}