{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:43C4BJL4AHW3VP25AHNOKNV6KY","short_pith_number":"pith:43C4BJL4","canonical_record":{"source":{"id":"2202.11154","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-22T20:17:46Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"e6a05e6b3d8c9c8b4c62958e50d9c9dbfbad2fbd1d4d946d826c7e410bf7503e","abstract_canon_sha256":"73390655f0c155aec5b43e2de6dc3c814a2472525dd8ebb79f96b597c9b13624"},"schema_version":"1.0"},"canonical_sha256":"e6c5c0a57c01edbabf5d01dae536be56358a627be32b4d9f583b333fc9e25286","source":{"kind":"arxiv","id":"2202.11154","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.11154","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"2202.11154v2","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.11154","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"43C4BJL4AHW3","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"pith_short_16","alias_value":"43C4BJL4AHW3VP25","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"pith_short_8","alias_value":"43C4BJL4","created_at":"2026-07-05T04:10:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:43C4BJL4AHW3VP25AHNOKNV6KY","target":"record","payload":{"canonical_record":{"source":{"id":"2202.11154","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-22T20:17:46Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"e6a05e6b3d8c9c8b4c62958e50d9c9dbfbad2fbd1d4d946d826c7e410bf7503e","abstract_canon_sha256":"73390655f0c155aec5b43e2de6dc3c814a2472525dd8ebb79f96b597c9b13624"},"schema_version":"1.0"},"canonical_sha256":"e6c5c0a57c01edbabf5d01dae536be56358a627be32b4d9f583b333fc9e25286","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:10:01.346273Z","signature_b64":"E0yFUU3Wieu/26Jr4zY55mAI45a0u+pvj4/oMpCV8+Wer4pRjoZZctOWs9+V8fAxbcwAjof0s5odJ62GE8nyCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6c5c0a57c01edbabf5d01dae536be56358a627be32b4d9f583b333fc9e25286","last_reissued_at":"2026-07-05T04:10:01.345797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:10:01.345797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.11154","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":"2026-07-05T04:10:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dd5AscRAHK1XNqzI6rqE/rddFN8SwCSQ4cirrGuWbscDl1cmbgwPxoGAOiVP0o19dFS3eMJ1fAfq3KJ0bz7CAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T19:41:10.489870Z"},"content_sha256":"ec34aeb938efbd90d9f094b0d3e8df58e092be76915ae4f8285da45d3a638139","schema_version":"1.0","event_id":"sha256:ec34aeb938efbd90d9f094b0d3e8df58e092be76915ae4f8285da45d3a638139"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:43C4BJL4AHW3VP25AHNOKNV6KY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parallel MCMC Without Embarrassing Failures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Daniel Augusto de Souza, Diego Mesquita, Luigi Acerbi, Samuel Kaski","submitted_at":"2022-02-22T20:17:46Z","abstract_excerpt":"Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run in parallel on (sub)posteriors defined on data partitions. Then, a server combines local results. While efficient, this framework is very sensitive to the quality of subposterior sampling. Common sampling problems such as missing modes or misrepresentation of low-density regions are amplified -- instead of being corrected -- in the combination phase, leading to catastrophic failures. In this work, we propose a novel c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.11154","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/2202.11154/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":"2026-07-05T04:10:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WSEnkELKsK7aOmueU/bxtX1dGEKIWfmLrWLpUVfmDrSOc7yRgJ8W0Swbu3JwfL71ZTcPWYIbBLbGjPp86EV+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T19:41:10.490395Z"},"content_sha256":"3e243890492f3b50bd2af7484a4aff1f55230e6d62f70f0ed4e3764ee85c0b2c","schema_version":"1.0","event_id":"sha256:3e243890492f3b50bd2af7484a4aff1f55230e6d62f70f0ed4e3764ee85c0b2c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/43C4BJL4AHW3VP25AHNOKNV6KY/bundle.json","state_url":"https://pith.science/pith/43C4BJL4AHW3VP25AHNOKNV6KY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/43C4BJL4AHW3VP25AHNOKNV6KY/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":"2026-08-10T19:41:10Z","links":{"resolver":"https://pith.science/pith/43C4BJL4AHW3VP25AHNOKNV6KY","bundle":"https://pith.science/pith/43C4BJL4AHW3VP25AHNOKNV6KY/bundle.json","state":"https://pith.science/pith/43C4BJL4AHW3VP25AHNOKNV6KY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/43C4BJL4AHW3VP25AHNOKNV6KY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:43C4BJL4AHW3VP25AHNOKNV6KY","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":"73390655f0c155aec5b43e2de6dc3c814a2472525dd8ebb79f96b597c9b13624","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-22T20:17:46Z","title_canon_sha256":"e6a05e6b3d8c9c8b4c62958e50d9c9dbfbad2fbd1d4d946d826c7e410bf7503e"},"schema_version":"1.0","source":{"id":"2202.11154","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.11154","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"2202.11154v2","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.11154","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"43C4BJL4AHW3","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"pith_short_16","alias_value":"43C4BJL4AHW3VP25","created_at":"2026-07-05T04:10:01Z"},{"alias_kind":"pith_short_8","alias_value":"43C4BJL4","created_at":"2026-07-05T04:10:01Z"}],"graph_snapshots":[{"event_id":"sha256:3e243890492f3b50bd2af7484a4aff1f55230e6d62f70f0ed4e3764ee85c0b2c","target":"graph","created_at":"2026-07-05T04:10:01Z","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/2202.11154/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run in parallel on (sub)posteriors defined on data partitions. Then, a server combines local results. While efficient, this framework is very sensitive to the quality of subposterior sampling. Common sampling problems such as missing modes or misrepresentation of low-density regions are amplified -- instead of being corrected -- in the combination phase, leading to catastrophic failures. In this work, we propose a novel c","authors_text":"Daniel Augusto de Souza, Diego Mesquita, Luigi Acerbi, Samuel Kaski","cross_cats":["cs.LG","stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-22T20:17:46Z","title":"Parallel MCMC Without Embarrassing Failures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.11154","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:ec34aeb938efbd90d9f094b0d3e8df58e092be76915ae4f8285da45d3a638139","target":"record","created_at":"2026-07-05T04:10:01Z","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":"73390655f0c155aec5b43e2de6dc3c814a2472525dd8ebb79f96b597c9b13624","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-22T20:17:46Z","title_canon_sha256":"e6a05e6b3d8c9c8b4c62958e50d9c9dbfbad2fbd1d4d946d826c7e410bf7503e"},"schema_version":"1.0","source":{"id":"2202.11154","kind":"arxiv","version":2}},"canonical_sha256":"e6c5c0a57c01edbabf5d01dae536be56358a627be32b4d9f583b333fc9e25286","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e6c5c0a57c01edbabf5d01dae536be56358a627be32b4d9f583b333fc9e25286","first_computed_at":"2026-07-05T04:10:01.345797Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:10:01.345797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E0yFUU3Wieu/26Jr4zY55mAI45a0u+pvj4/oMpCV8+Wer4pRjoZZctOWs9+V8fAxbcwAjof0s5odJ62GE8nyCg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:10:01.346273Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.11154","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec34aeb938efbd90d9f094b0d3e8df58e092be76915ae4f8285da45d3a638139","sha256:3e243890492f3b50bd2af7484a4aff1f55230e6d62f70f0ed4e3764ee85c0b2c"],"state_sha256":"ae01c03eda0e737be9803243d66338969d813ebe9a7a39523b07199284535e22"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r1hz/Oc0mGZ422N0y9zZdghnVGEd+uLGC7N/STkTpzVGNPaaMbwa7pB7kaNz+0heeq3Yl064YXqUXQuGGxpADw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T19:41:10.494109Z","bundle_sha256":"2b60d19d4eb54a6e7339d2df8fbcfabe0447c08804431482cbce8f6373857885"}}