{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QWSHOX262OV7IYYXQNMNWT3GS5","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":"c4dab38f7279e6dc5ba9c2e7a73953d9f66508709a79c0c3c25e4668320b30b9","cross_cats_sorted":["cs.LG","stat.CO","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-09-25T14:11:32Z","title_canon_sha256":"6525a620948e0f7bb01b766a173fcd799f0e22b12ab9601190e3818b92aad443"},"schema_version":"1.0","source":{"id":"1809.09505","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.09505","created_at":"2026-05-18T00:04:19Z"},{"alias_kind":"arxiv_version","alias_value":"1809.09505v2","created_at":"2026-05-18T00:04:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.09505","created_at":"2026-05-18T00:04:19Z"},{"alias_kind":"pith_short_12","alias_value":"QWSHOX262OV7","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"QWSHOX262OV7IYYX","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"QWSHOX26","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:966519233a534d62c1a04f3f2d7c1489d9e4483c2afe4bae5ac2ffabf33b25f2","target":"graph","created_at":"2026-05-18T00:04:19Z","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":"Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates. Classical Monte Carlo methods, particularly Markov Chain Monte Carlo, remain the gold standard for approximate Bayesian inference because they have a robust finite-sample theory and reliable convergence diagnostics. However, alternative methods, which are more scalable or apply to problems where Markov Chain Monte Carlo cannot be used, lack the same f","authors_text":"Jonathan H. Huggins, Miko{\\l}aj Kasprzak, Tamara Broderick, Trevor Campbell","cross_cats":["cs.LG","stat.CO","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-09-25T14:11:32Z","title":"Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.09505","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:f6924bfe174f76bb7eeb878dc949d4b41aae9295ef11ba442bb1eb36944a394f","target":"record","created_at":"2026-05-18T00:04:19Z","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":"c4dab38f7279e6dc5ba9c2e7a73953d9f66508709a79c0c3c25e4668320b30b9","cross_cats_sorted":["cs.LG","stat.CO","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-09-25T14:11:32Z","title_canon_sha256":"6525a620948e0f7bb01b766a173fcd799f0e22b12ab9601190e3818b92aad443"},"schema_version":"1.0","source":{"id":"1809.09505","kind":"arxiv","version":2}},"canonical_sha256":"85a4775f5ed3abf463178358db4f66974bf3db5c150218e4509f68c75dbb9bd2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85a4775f5ed3abf463178358db4f66974bf3db5c150218e4509f68c75dbb9bd2","first_computed_at":"2026-05-18T00:04:19.884090Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:19.884090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v01gsWpiPxDnsQuxGwX6flnD/kJrlt4/sl1brSd4bJLfWgK9wi3PWiovpOy9fbNsM1DVJOaiTMCDE1TIhdjtCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:19.884718Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.09505","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f6924bfe174f76bb7eeb878dc949d4b41aae9295ef11ba442bb1eb36944a394f","sha256:966519233a534d62c1a04f3f2d7c1489d9e4483c2afe4bae5ac2ffabf33b25f2"],"state_sha256":"36a94b827ea202743cfd01d07545c6857c350b05a898238866e087fde8fa6806"}