{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:VVCLARJJCC7AHBSSPV5LZZQRIN","short_pith_number":"pith:VVCLARJJ","canonical_record":{"source":{"id":"1607.02788","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-07-10T21:46:43Z","cross_cats_sorted":["stat.AP","stat.ME"],"title_canon_sha256":"f51bc2fc88352f203f3f60242a6f421709d5767c5af72b0756e2ca4576a861af","abstract_canon_sha256":"12f336d1d26c701c097b3e33a0e31076e46afee36841710b9adf6b4d956331bf"},"schema_version":"1.0"},"canonical_sha256":"ad44b0452910be0386527d7abce6114354af15b62e89f49f596151b13ccfde7e","source":{"kind":"arxiv","id":"1607.02788","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.02788","created_at":"2026-05-18T00:27:22Z"},{"alias_kind":"arxiv_version","alias_value":"1607.02788v2","created_at":"2026-05-18T00:27:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.02788","created_at":"2026-05-18T00:27:22Z"},{"alias_kind":"pith_short_12","alias_value":"VVCLARJJCC7A","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"VVCLARJJCC7AHBSS","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"VVCLARJJ","created_at":"2026-05-18T12:30:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:VVCLARJJCC7AHBSSPV5LZZQRIN","target":"record","payload":{"canonical_record":{"source":{"id":"1607.02788","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-07-10T21:46:43Z","cross_cats_sorted":["stat.AP","stat.ME"],"title_canon_sha256":"f51bc2fc88352f203f3f60242a6f421709d5767c5af72b0756e2ca4576a861af","abstract_canon_sha256":"12f336d1d26c701c097b3e33a0e31076e46afee36841710b9adf6b4d956331bf"},"schema_version":"1.0"},"canonical_sha256":"ad44b0452910be0386527d7abce6114354af15b62e89f49f596151b13ccfde7e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:22.229445Z","signature_b64":"NpYDKXuB5eaOjSWFSINBiqxaCd4HYY8v37nUaoA7kQ0JgpQPRZwfNwfXlc7FF3UAj8flHO5i2NhESK16fPQmAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad44b0452910be0386527d7abce6114354af15b62e89f49f596151b13ccfde7e","last_reissued_at":"2026-05-18T00:27:22.228791Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:22.228791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1607.02788","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":"2026-05-18T00:27:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TBkx1N/DnuDB749an1+vPJo8YRxHJkL4LXVZ1O/FF1/K18LHEvr99CdeZ17g5pd+cVS3EyXLXsKpbdeKE21YDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T10:36:43.808958Z"},"content_sha256":"50f709d1d5fc53e36689ffbec2dec151d1fd77de27cc5a20bf5114fe0e6584be","schema_version":"1.0","event_id":"sha256:50f709d1d5fc53e36689ffbec2dec151d1fd77de27cc5a20bf5114fe0e6584be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:VVCLARJJCC7AHBSSPV5LZZQRIN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parallel local approximation MCMC for expensive models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"stat.CO","authors_text":"Aaron Smith, Andrew Davis, Natesh Pillai, Patrick Conrad, Youssef Marzouk","submitted_at":"2016-07-10T21:46:43Z","abstract_excerpt":"Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model, such as a system of partial differential equations. In recent work [Conrad et al. JASA 2016, arXiv:1402.1694], we described a framework for constructing and refining local approximations of such models during an MCMC simulation. These posterior--adapted approximations harness regularity of the model to reduce the computational cost of inference while preserving asymptotic exactness of the Markov chain. Here we desc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.02788","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":""},"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":"2026-05-18T00:27:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VjCIraNH+dp7uIDkSVKsT6U7D4y2xCE0IH/uMnDHq81EUMkzbH2y586abuQdw3XJkjacLay+CjpDQceUriYNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T10:36:43.809698Z"},"content_sha256":"7d91e789cb33a08dbaaff1df7cbd7dcadad4ecba1737187b83d304974a9bdaa9","schema_version":"1.0","event_id":"sha256:7d91e789cb33a08dbaaff1df7cbd7dcadad4ecba1737187b83d304974a9bdaa9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VVCLARJJCC7AHBSSPV5LZZQRIN/bundle.json","state_url":"https://pith.science/pith/VVCLARJJCC7AHBSSPV5LZZQRIN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VVCLARJJCC7AHBSSPV5LZZQRIN/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":"2026-05-26T10:36:43Z","links":{"resolver":"https://pith.science/pith/VVCLARJJCC7AHBSSPV5LZZQRIN","bundle":"https://pith.science/pith/VVCLARJJCC7AHBSSPV5LZZQRIN/bundle.json","state":"https://pith.science/pith/VVCLARJJCC7AHBSSPV5LZZQRIN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VVCLARJJCC7AHBSSPV5LZZQRIN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:VVCLARJJCC7AHBSSPV5LZZQRIN","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":"12f336d1d26c701c097b3e33a0e31076e46afee36841710b9adf6b4d956331bf","cross_cats_sorted":["stat.AP","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-07-10T21:46:43Z","title_canon_sha256":"f51bc2fc88352f203f3f60242a6f421709d5767c5af72b0756e2ca4576a861af"},"schema_version":"1.0","source":{"id":"1607.02788","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.02788","created_at":"2026-05-18T00:27:22Z"},{"alias_kind":"arxiv_version","alias_value":"1607.02788v2","created_at":"2026-05-18T00:27:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.02788","created_at":"2026-05-18T00:27:22Z"},{"alias_kind":"pith_short_12","alias_value":"VVCLARJJCC7A","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"VVCLARJJCC7AHBSS","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"VVCLARJJ","created_at":"2026-05-18T12:30:48Z"}],"graph_snapshots":[{"event_id":"sha256:7d91e789cb33a08dbaaff1df7cbd7dcadad4ecba1737187b83d304974a9bdaa9","target":"graph","created_at":"2026-05-18T00:27:22Z","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":"Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model, such as a system of partial differential equations. In recent work [Conrad et al. JASA 2016, arXiv:1402.1694], we described a framework for constructing and refining local approximations of such models during an MCMC simulation. These posterior--adapted approximations harness regularity of the model to reduce the computational cost of inference while preserving asymptotic exactness of the Markov chain. Here we desc","authors_text":"Aaron Smith, Andrew Davis, Natesh Pillai, Patrick Conrad, Youssef Marzouk","cross_cats":["stat.AP","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-07-10T21:46:43Z","title":"Parallel local approximation MCMC for expensive models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.02788","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":"sha256:50f709d1d5fc53e36689ffbec2dec151d1fd77de27cc5a20bf5114fe0e6584be","target":"record","created_at":"2026-05-18T00:27:22Z","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":"12f336d1d26c701c097b3e33a0e31076e46afee36841710b9adf6b4d956331bf","cross_cats_sorted":["stat.AP","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-07-10T21:46:43Z","title_canon_sha256":"f51bc2fc88352f203f3f60242a6f421709d5767c5af72b0756e2ca4576a861af"},"schema_version":"1.0","source":{"id":"1607.02788","kind":"arxiv","version":2}},"canonical_sha256":"ad44b0452910be0386527d7abce6114354af15b62e89f49f596151b13ccfde7e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ad44b0452910be0386527d7abce6114354af15b62e89f49f596151b13ccfde7e","first_computed_at":"2026-05-18T00:27:22.228791Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:22.228791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NpYDKXuB5eaOjSWFSINBiqxaCd4HYY8v37nUaoA7kQ0JgpQPRZwfNwfXlc7FF3UAj8flHO5i2NhESK16fPQmAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:22.229445Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.02788","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:50f709d1d5fc53e36689ffbec2dec151d1fd77de27cc5a20bf5114fe0e6584be","sha256:7d91e789cb33a08dbaaff1df7cbd7dcadad4ecba1737187b83d304974a9bdaa9"],"state_sha256":"24c2177803fe2b09850f524b25d8b61199289e6cb38ce3aaa6122d1ef9dd3c80"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nz/ccIG8LJQVDWAZcX0ZQAS2pB+bg06b8nlfw+9FypzsCKXykqgzbTlxfoHJnZUlzqzmWXQ/OOy74/egVJ3MDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T10:36:43.813699Z","bundle_sha256":"96aeb879b947b80eda5b3274c0c5ea8db6d33743564e5f047bf7a17e7a6bdbb2"}}