{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:ZW5DMGSYWHX3AHXGJFX4SVG6DW","short_pith_number":"pith:ZW5DMGSY","canonical_record":{"source":{"id":"1510.05078","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-17T06:48:48Z","cross_cats_sorted":[],"title_canon_sha256":"9de47324ac934c812fe1e0c85b0bb1a5863dc64603c0602fa97059debfdc66d3","abstract_canon_sha256":"3dcafb718fe1c6c6f3d90385d32fbe4435f31f751b4eff4e6f4ccd3af561b8ce"},"schema_version":"1.0"},"canonical_sha256":"cdba361a58b1efb01ee6496fc954de1d832608e1a8f09376bced81248155793c","source":{"kind":"arxiv","id":"1510.05078","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.05078","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"arxiv_version","alias_value":"1510.05078v3","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.05078","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"pith_short_12","alias_value":"ZW5DMGSYWHX3","created_at":"2026-05-18T12:29:52Z"},{"alias_kind":"pith_short_16","alias_value":"ZW5DMGSYWHX3AHXG","created_at":"2026-05-18T12:29:52Z"},{"alias_kind":"pith_short_8","alias_value":"ZW5DMGSY","created_at":"2026-05-18T12:29:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:ZW5DMGSYWHX3AHXGJFX4SVG6DW","target":"record","payload":{"canonical_record":{"source":{"id":"1510.05078","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-17T06:48:48Z","cross_cats_sorted":[],"title_canon_sha256":"9de47324ac934c812fe1e0c85b0bb1a5863dc64603c0602fa97059debfdc66d3","abstract_canon_sha256":"3dcafb718fe1c6c6f3d90385d32fbe4435f31f751b4eff4e6f4ccd3af561b8ce"},"schema_version":"1.0"},"canonical_sha256":"cdba361a58b1efb01ee6496fc954de1d832608e1a8f09376bced81248155793c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:05:12.679890Z","signature_b64":"ZhVeTWNK1bTL3ijGVLFw4Lj1F8xKxrQZ64ASl5v0tt8+PUol1q//9sK0aQrn/+KF2vM3W3yUdYM5pS02mZnrCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdba361a58b1efb01ee6496fc954de1d832608e1a8f09376bced81248155793c","last_reissued_at":"2026-05-18T01:05:12.679103Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:05:12.679103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1510.05078","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:05:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MjaXgt9XYxw5Bn7PD2yx5SathF8X9hwauu4l54hEq7U2RAMFXj1Zw2/kJlGsJYxAHEFa+zjA6gKuvqQ94wMoDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T15:20:44.440483Z"},"content_sha256":"df6984ab3f08a48725144c60ef6d90da29d67b3c1fdafadf7ad80330e0049a6c","schema_version":"1.0","event_id":"sha256:df6984ab3f08a48725144c60ef6d90da29d67b3c1fdafadf7ad80330e0049a6c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:ZW5DMGSYWHX3AHXGJFX4SVG6DW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A General Method for Robust Bayesian Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Chong Wang, David M. Blei","submitted_at":"2015-10-17T06:48:48Z","abstract_excerpt":"Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and bursty topic models. In this paper we develop a general approach to robust Bayesian modeling. We show how to turn an existing Bayesian model into a robust model, and then develop a generic strategy for computing with it. We use our method to study robust variants of several models, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.05078","kind":"arxiv","version":3},"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":"2026-05-18T01:05:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YPDQ6FPRrGezh0pTB0R7UtkP5g2IhEzMTvYieqG6niCJp3++5alZGhL95goRz+alTn1icmpFz5tiT+BOBoeHDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T15:20:44.441103Z"},"content_sha256":"eb98bf052a7731ac736b32b2031f92382231bbae7788856e0b096b5a732a5d11","schema_version":"1.0","event_id":"sha256:eb98bf052a7731ac736b32b2031f92382231bbae7788856e0b096b5a732a5d11"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZW5DMGSYWHX3AHXGJFX4SVG6DW/bundle.json","state_url":"https://pith.science/pith/ZW5DMGSYWHX3AHXGJFX4SVG6DW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZW5DMGSYWHX3AHXGJFX4SVG6DW/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":"2026-06-01T15:20:44Z","links":{"resolver":"https://pith.science/pith/ZW5DMGSYWHX3AHXGJFX4SVG6DW","bundle":"https://pith.science/pith/ZW5DMGSYWHX3AHXGJFX4SVG6DW/bundle.json","state":"https://pith.science/pith/ZW5DMGSYWHX3AHXGJFX4SVG6DW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZW5DMGSYWHX3AHXGJFX4SVG6DW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:ZW5DMGSYWHX3AHXGJFX4SVG6DW","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":"3dcafb718fe1c6c6f3d90385d32fbe4435f31f751b4eff4e6f4ccd3af561b8ce","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-17T06:48:48Z","title_canon_sha256":"9de47324ac934c812fe1e0c85b0bb1a5863dc64603c0602fa97059debfdc66d3"},"schema_version":"1.0","source":{"id":"1510.05078","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.05078","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"arxiv_version","alias_value":"1510.05078v3","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.05078","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"pith_short_12","alias_value":"ZW5DMGSYWHX3","created_at":"2026-05-18T12:29:52Z"},{"alias_kind":"pith_short_16","alias_value":"ZW5DMGSYWHX3AHXG","created_at":"2026-05-18T12:29:52Z"},{"alias_kind":"pith_short_8","alias_value":"ZW5DMGSY","created_at":"2026-05-18T12:29:52Z"}],"graph_snapshots":[{"event_id":"sha256:eb98bf052a7731ac736b32b2031f92382231bbae7788856e0b096b5a732a5d11","target":"graph","created_at":"2026-05-18T01:05:12Z","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":"Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and bursty topic models. In this paper we develop a general approach to robust Bayesian modeling. We show how to turn an existing Bayesian model into a robust model, and then develop a generic strategy for computing with it. We use our method to study robust variants of several models, ","authors_text":"Chong Wang, David M. Blei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-17T06:48:48Z","title":"A General Method for Robust Bayesian Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.05078","kind":"arxiv","version":3},"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:df6984ab3f08a48725144c60ef6d90da29d67b3c1fdafadf7ad80330e0049a6c","target":"record","created_at":"2026-05-18T01:05:12Z","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":"3dcafb718fe1c6c6f3d90385d32fbe4435f31f751b4eff4e6f4ccd3af561b8ce","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-17T06:48:48Z","title_canon_sha256":"9de47324ac934c812fe1e0c85b0bb1a5863dc64603c0602fa97059debfdc66d3"},"schema_version":"1.0","source":{"id":"1510.05078","kind":"arxiv","version":3}},"canonical_sha256":"cdba361a58b1efb01ee6496fc954de1d832608e1a8f09376bced81248155793c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cdba361a58b1efb01ee6496fc954de1d832608e1a8f09376bced81248155793c","first_computed_at":"2026-05-18T01:05:12.679103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:05:12.679103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZhVeTWNK1bTL3ijGVLFw4Lj1F8xKxrQZ64ASl5v0tt8+PUol1q//9sK0aQrn/+KF2vM3W3yUdYM5pS02mZnrCA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:05:12.679890Z","signed_message":"canonical_sha256_bytes"},"source_id":"1510.05078","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:df6984ab3f08a48725144c60ef6d90da29d67b3c1fdafadf7ad80330e0049a6c","sha256:eb98bf052a7731ac736b32b2031f92382231bbae7788856e0b096b5a732a5d11"],"state_sha256":"0763ca61948dabed15c6561579539a863ff3cb79d2f69dde9e80dacb9ed4845d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HCxvsxlPag2MuJfAGdse9uUyTLa/Acdpv9KyVrlCx729yBI6aYyo0yGwJHcVwAo0tqDdHtuSbXi8wf5UIHNOCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T15:20:44.444381Z","bundle_sha256":"45de19a6fb925c343226f250a43f6c2876e05ccfd3b87d777e30f9610d73cd59"}}