{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:RITQ3W6HXIKNJCJKVRFADP62ZL","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":"137101f439290cd14e6402427774fe35259a6f2daf60741be7a0c1defb63ded6","cross_cats_sorted":["math.PR","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-08-28T16:04:08Z","title_canon_sha256":"6cb7bf81b8dd72a37b5f8ebbb905d8b8bb1a5d609caa6907c470f7e3d78e75ee"},"schema_version":"1.0","source":{"id":"1808.09379","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.09379","created_at":"2026-05-18T00:07:01Z"},{"alias_kind":"arxiv_version","alias_value":"1808.09379v1","created_at":"2026-05-18T00:07:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.09379","created_at":"2026-05-18T00:07:01Z"},{"alias_kind":"pith_short_12","alias_value":"RITQ3W6HXIKN","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RITQ3W6HXIKNJCJK","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RITQ3W6H","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:50cb1328269020ddaf105511426034ec92f28714941f7345bdcf799540cc4bef","target":"graph","created_at":"2026-05-18T00:07:01Z","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"},"paper":{"abstract_excerpt":"Markov chain Monte Carlo (MCMC) sampling of posterior distributions arising in Bayesian inverse problems is challenging when evaluations of the forward model are computationally expensive. Replacing the forward model with a low-cost, low-fidelity model often significantly reduces computational cost; however, employing a low-fidelity model alone means that the stationary distribution of the MCMC chain is the posterior distribution corresponding to the low-fidelity model, rather than the original posterior distribution corresponding to the high-fidelity model. We propose a multifidelity approach","authors_text":"Benjamin Peherstorfer, Youssef Marzouk","cross_cats":["math.PR","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-08-28T16:04:08Z","title":"A transport-based multifidelity preconditioner for Markov chain Monte Carlo"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.09379","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":"sha256:4afa38dbe2e9a3e4f0a8412d83228849204a28c50358b9191c233bb403dca334","target":"record","created_at":"2026-05-18T00:07:01Z","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":"137101f439290cd14e6402427774fe35259a6f2daf60741be7a0c1defb63ded6","cross_cats_sorted":["math.PR","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-08-28T16:04:08Z","title_canon_sha256":"6cb7bf81b8dd72a37b5f8ebbb905d8b8bb1a5d609caa6907c470f7e3d78e75ee"},"schema_version":"1.0","source":{"id":"1808.09379","kind":"arxiv","version":1}},"canonical_sha256":"8a270ddbc7ba14d4892aac4a01bfdacae497406560c7a3b3a6c4fa1f99346370","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a270ddbc7ba14d4892aac4a01bfdacae497406560c7a3b3a6c4fa1f99346370","first_computed_at":"2026-05-18T00:07:01.541773Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:01.541773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rmNSxgwAL86JZ0JmztwFopTTkdWK6peaVS72zaGvHH2tj3CAMmoT5vMm6B8JEEYVj9TqfSpiN81dnlb2Fz6oDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:01.542340Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.09379","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4afa38dbe2e9a3e4f0a8412d83228849204a28c50358b9191c233bb403dca334","sha256:50cb1328269020ddaf105511426034ec92f28714941f7345bdcf799540cc4bef"],"state_sha256":"114f2df04f7903c681b2371c58e160cd04ca4408f92d9635074c8264515e166c"}