{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:27OETM3FFNDC36X6WRQ223P63O","short_pith_number":"pith:27OETM3F","canonical_record":{"source":{"id":"2106.12652","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2021-06-23T20:40:27Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"222d3dea0efce08a7295806a3175a974e61db5da1912197401875e5e67aa5404","abstract_canon_sha256":"8cc2b8a96d1323557d52fe3da9a57f2073909875815ece06b115df034937f79d"},"schema_version":"1.0"},"canonical_sha256":"d7dc49b3652b462dfafeb461ad6dfedbb4fdedb64847c7c807853ab5a38c5331","source":{"kind":"arxiv","id":"2106.12652","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.12652","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"arxiv_version","alias_value":"2106.12652v2","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.12652","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"pith_short_12","alias_value":"27OETM3FFNDC","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"pith_short_16","alias_value":"27OETM3FFNDC36X6","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"pith_short_8","alias_value":"27OETM3F","created_at":"2026-07-05T04:08:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:27OETM3FFNDC36X6WRQ223P63O","target":"record","payload":{"canonical_record":{"source":{"id":"2106.12652","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2021-06-23T20:40:27Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"222d3dea0efce08a7295806a3175a974e61db5da1912197401875e5e67aa5404","abstract_canon_sha256":"8cc2b8a96d1323557d52fe3da9a57f2073909875815ece06b115df034937f79d"},"schema_version":"1.0"},"canonical_sha256":"d7dc49b3652b462dfafeb461ad6dfedbb4fdedb64847c7c807853ab5a38c5331","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:08:57.599940Z","signature_b64":"zkucp1mkYiW5WsfPWEadt6kGAgPw/5pN2Azm+DXEyOFTPmJDvTPD/RbQiqcTvuLS2bM6rS+fbhm3ewY2FotaCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7dc49b3652b462dfafeb461ad6dfedbb4fdedb64847c7c807853ab5a38c5331","last_reissued_at":"2026-07-05T04:08:57.599556Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:08:57.599556Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.12652","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:08:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2RIzxJ6rn9L5f6D/WrrEXJ1eOw0Jdkw632GBsG6c+8VqGXyTms0qKPJGFlBt/Fd4yJApyS/63kbzcevHQRrwBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T23:24:38.079712Z"},"content_sha256":"302ed3e0972f6ee413467b0b5a226f5eda3a1269492a74544f8e2d586877363e","schema_version":"1.0","event_id":"sha256:302ed3e0972f6ee413467b0b5a226f5eda3a1269492a74544f8e2d586877363e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:27OETM3FFNDC36X6WRQ223P63O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Black Box Variational Bayesian Model Averaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Mookyong Son, Shrijita Bhattacharya, Tapabrata Maiti, Vojtech Kejzlar","submitted_at":"2021-06-23T20:40:27Z","abstract_excerpt":"For many decades now, Bayesian Model Averaging (BMA) has been a popular framework to systematically account for model uncertainty that arises in situations when multiple competing models are available to describe the same or similar physical process. The implementation of this framework, however, comes with a multitude of practical challenges including posterior approximation via Markov Chain Monte Carlo and numerical integration. We present a Variational Bayesian Inference approach to BMA as a viable alternative to the standard solutions which avoids many of the aforementioned pitfalls. The p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.12652","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2106.12652/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T04:08:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hK+bDdatYPKLUxq4E9mOMdrALlH4MjXfDo83inZOreZaoYHohn8lWlwciLE3NM1DUDEqOT+zxP5ZwQi6MKw9Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T23:24:38.080466Z"},"content_sha256":"b5ce9eef1c34c97e6db0c713713417bcdacfe25abcda260a845f6cc23aea0157","schema_version":"1.0","event_id":"sha256:b5ce9eef1c34c97e6db0c713713417bcdacfe25abcda260a845f6cc23aea0157"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/27OETM3FFNDC36X6WRQ223P63O/bundle.json","state_url":"https://pith.science/pith/27OETM3FFNDC36X6WRQ223P63O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/27OETM3FFNDC36X6WRQ223P63O/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":"2026-08-19T23:24:38Z","links":{"resolver":"https://pith.science/pith/27OETM3FFNDC36X6WRQ223P63O","bundle":"https://pith.science/pith/27OETM3FFNDC36X6WRQ223P63O/bundle.json","state":"https://pith.science/pith/27OETM3FFNDC36X6WRQ223P63O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/27OETM3FFNDC36X6WRQ223P63O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:27OETM3FFNDC36X6WRQ223P63O","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":"8cc2b8a96d1323557d52fe3da9a57f2073909875815ece06b115df034937f79d","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2021-06-23T20:40:27Z","title_canon_sha256":"222d3dea0efce08a7295806a3175a974e61db5da1912197401875e5e67aa5404"},"schema_version":"1.0","source":{"id":"2106.12652","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.12652","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"arxiv_version","alias_value":"2106.12652v2","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.12652","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"pith_short_12","alias_value":"27OETM3FFNDC","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"pith_short_16","alias_value":"27OETM3FFNDC36X6","created_at":"2026-07-05T04:08:57Z"},{"alias_kind":"pith_short_8","alias_value":"27OETM3F","created_at":"2026-07-05T04:08:57Z"}],"graph_snapshots":[{"event_id":"sha256:b5ce9eef1c34c97e6db0c713713417bcdacfe25abcda260a845f6cc23aea0157","target":"graph","created_at":"2026-07-05T04:08:57Z","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/2106.12652/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"For many decades now, Bayesian Model Averaging (BMA) has been a popular framework to systematically account for model uncertainty that arises in situations when multiple competing models are available to describe the same or similar physical process. The implementation of this framework, however, comes with a multitude of practical challenges including posterior approximation via Markov Chain Monte Carlo and numerical integration. We present a Variational Bayesian Inference approach to BMA as a viable alternative to the standard solutions which avoids many of the aforementioned pitfalls. The p","authors_text":"Mookyong Son, Shrijita Bhattacharya, Tapabrata Maiti, Vojtech Kejzlar","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2021-06-23T20:40:27Z","title":"Black Box Variational Bayesian Model Averaging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.12652","kind":"arxiv","version":2},"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":"sha256:302ed3e0972f6ee413467b0b5a226f5eda3a1269492a74544f8e2d586877363e","target":"record","created_at":"2026-07-05T04:08:57Z","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":"8cc2b8a96d1323557d52fe3da9a57f2073909875815ece06b115df034937f79d","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2021-06-23T20:40:27Z","title_canon_sha256":"222d3dea0efce08a7295806a3175a974e61db5da1912197401875e5e67aa5404"},"schema_version":"1.0","source":{"id":"2106.12652","kind":"arxiv","version":2}},"canonical_sha256":"d7dc49b3652b462dfafeb461ad6dfedbb4fdedb64847c7c807853ab5a38c5331","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7dc49b3652b462dfafeb461ad6dfedbb4fdedb64847c7c807853ab5a38c5331","first_computed_at":"2026-07-05T04:08:57.599556Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:08:57.599556Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zkucp1mkYiW5WsfPWEadt6kGAgPw/5pN2Azm+DXEyOFTPmJDvTPD/RbQiqcTvuLS2bM6rS+fbhm3ewY2FotaCg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:08:57.599940Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.12652","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:302ed3e0972f6ee413467b0b5a226f5eda3a1269492a74544f8e2d586877363e","sha256:b5ce9eef1c34c97e6db0c713713417bcdacfe25abcda260a845f6cc23aea0157"],"state_sha256":"cbabd40db2f78b7a809187446ffce9c58f06170b43e6f1382f574da688ef3c4c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L4Qot6haWMQ2b87kitKztM7JwI/52mZ2m9kO5MQrvv5dP4dcue6UoK1rxnkV32xcWt0xnKwdVmkty/HCyDU8Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T23:24:38.084949Z","bundle_sha256":"93c801c403b4e04d464287c2006231778454c3b4f985ceb2035bbae0dabd8271"}}