{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:OFPK2E2NIMO5BPL37HZ7FTEXSB","short_pith_number":"pith:OFPK2E2N","canonical_record":{"source":{"id":"1905.06680","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-16T12:08:50Z","cross_cats_sorted":[],"title_canon_sha256":"2362168362d881442ab13fe90e885dee1e58849276f7eb97c67aa9ecf1189857","abstract_canon_sha256":"f0da00385e44d6a9fb7b58e13121b2cd1ab1b3e2321a007ff89b41a9f38f91a6"},"schema_version":"1.0"},"canonical_sha256":"715ead134d431dd0bd7bf9f3f2cc97905416d209a9d913a3ee8e94329863a5c3","source":{"kind":"arxiv","id":"1905.06680","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.06680","created_at":"2026-05-17T23:46:01Z"},{"alias_kind":"arxiv_version","alias_value":"1905.06680v1","created_at":"2026-05-17T23:46:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06680","created_at":"2026-05-17T23:46:01Z"},{"alias_kind":"pith_short_12","alias_value":"OFPK2E2NIMO5","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"OFPK2E2NIMO5BPL3","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"OFPK2E2N","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:OFPK2E2NIMO5BPL37HZ7FTEXSB","target":"record","payload":{"canonical_record":{"source":{"id":"1905.06680","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-16T12:08:50Z","cross_cats_sorted":[],"title_canon_sha256":"2362168362d881442ab13fe90e885dee1e58849276f7eb97c67aa9ecf1189857","abstract_canon_sha256":"f0da00385e44d6a9fb7b58e13121b2cd1ab1b3e2321a007ff89b41a9f38f91a6"},"schema_version":"1.0"},"canonical_sha256":"715ead134d431dd0bd7bf9f3f2cc97905416d209a9d913a3ee8e94329863a5c3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:01.416854Z","signature_b64":"271GKK3/Pu38gUfFL1WarHbRPyN24P+F85xnoySvN8GHAoovC4JtereT8Lpt40q0xQhw++O4Fa0IlcD8LBudDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"715ead134d431dd0bd7bf9f3f2cc97905416d209a9d913a3ee8e94329863a5c3","last_reissued_at":"2026-05-17T23:46:01.416132Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:01.416132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.06680","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cpbhwOCliCChguBRmZCEN6dwjxMeb5+uJFQhUvu2LRYwuZ8xgp465q/KSCE5sf+3aVme2/DQE6CNp/5JbDlQBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T22:45:55.466475Z"},"content_sha256":"e66aceaaffcdce51bf3ad1b787b36e1ab6de351e400e6b5150582186948e167a","schema_version":"1.0","event_id":"sha256:e66aceaaffcdce51bf3ad1b787b36e1ab6de351e400e6b5150582186948e167a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:OFPK2E2NIMO5BPL37HZ7FTEXSB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Evgeny Levi, Radu V. Craiu","submitted_at":"2019-05-16T12:08:50Z","abstract_excerpt":"With larger data at their disposal, scientists are emboldened to tackle complex questions that require sophisticated statistical models. It is not unusual for the latter to have likelihood functions that elude analytical formulations. Even under such adversity, when one can simulate from the sampling distribution, Bayesian analysis can be conducted using approximate methods such as Approximate Bayesian Computation (ABC) or Bayesian Synthetic Likelihood (BSL). A significant drawback of these methods is that the number of required simulations can be prohibitively large, thus severely limiting th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06680","kind":"arxiv","version":1},"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":"2026-05-17T23:46:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5yM9C3YVSi9hbIMuZHSowVG5p/ejztqjdITlRvbVw74qK+S4ww6thznzbXyaCpykPxKIAcJPfMr4FMA/ITTNBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T22:45:55.467152Z"},"content_sha256":"13864e105333e4d68bcd19200e94fe0399fc6b0ca3af781daccf642d2651a8f8","schema_version":"1.0","event_id":"sha256:13864e105333e4d68bcd19200e94fe0399fc6b0ca3af781daccf642d2651a8f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OFPK2E2NIMO5BPL37HZ7FTEXSB/bundle.json","state_url":"https://pith.science/pith/OFPK2E2NIMO5BPL37HZ7FTEXSB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OFPK2E2NIMO5BPL37HZ7FTEXSB/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":"2026-05-29T22:45:55Z","links":{"resolver":"https://pith.science/pith/OFPK2E2NIMO5BPL37HZ7FTEXSB","bundle":"https://pith.science/pith/OFPK2E2NIMO5BPL37HZ7FTEXSB/bundle.json","state":"https://pith.science/pith/OFPK2E2NIMO5BPL37HZ7FTEXSB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OFPK2E2NIMO5BPL37HZ7FTEXSB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:OFPK2E2NIMO5BPL37HZ7FTEXSB","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":"f0da00385e44d6a9fb7b58e13121b2cd1ab1b3e2321a007ff89b41a9f38f91a6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-16T12:08:50Z","title_canon_sha256":"2362168362d881442ab13fe90e885dee1e58849276f7eb97c67aa9ecf1189857"},"schema_version":"1.0","source":{"id":"1905.06680","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.06680","created_at":"2026-05-17T23:46:01Z"},{"alias_kind":"arxiv_version","alias_value":"1905.06680v1","created_at":"2026-05-17T23:46:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06680","created_at":"2026-05-17T23:46:01Z"},{"alias_kind":"pith_short_12","alias_value":"OFPK2E2NIMO5","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"OFPK2E2NIMO5BPL3","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"OFPK2E2N","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:13864e105333e4d68bcd19200e94fe0399fc6b0ca3af781daccf642d2651a8f8","target":"graph","created_at":"2026-05-17T23:46:01Z","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":"With larger data at their disposal, scientists are emboldened to tackle complex questions that require sophisticated statistical models. It is not unusual for the latter to have likelihood functions that elude analytical formulations. Even under such adversity, when one can simulate from the sampling distribution, Bayesian analysis can be conducted using approximate methods such as Approximate Bayesian Computation (ABC) or Bayesian Synthetic Likelihood (BSL). A significant drawback of these methods is that the number of required simulations can be prohibitively large, thus severely limiting th","authors_text":"Evgeny Levi, Radu V. Craiu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-16T12:08:50Z","title":"Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06680","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:e66aceaaffcdce51bf3ad1b787b36e1ab6de351e400e6b5150582186948e167a","target":"record","created_at":"2026-05-17T23:46:01Z","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":"f0da00385e44d6a9fb7b58e13121b2cd1ab1b3e2321a007ff89b41a9f38f91a6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-05-16T12:08:50Z","title_canon_sha256":"2362168362d881442ab13fe90e885dee1e58849276f7eb97c67aa9ecf1189857"},"schema_version":"1.0","source":{"id":"1905.06680","kind":"arxiv","version":1}},"canonical_sha256":"715ead134d431dd0bd7bf9f3f2cc97905416d209a9d913a3ee8e94329863a5c3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"715ead134d431dd0bd7bf9f3f2cc97905416d209a9d913a3ee8e94329863a5c3","first_computed_at":"2026-05-17T23:46:01.416132Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:01.416132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"271GKK3/Pu38gUfFL1WarHbRPyN24P+F85xnoySvN8GHAoovC4JtereT8Lpt40q0xQhw++O4Fa0IlcD8LBudDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:01.416854Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.06680","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e66aceaaffcdce51bf3ad1b787b36e1ab6de351e400e6b5150582186948e167a","sha256:13864e105333e4d68bcd19200e94fe0399fc6b0ca3af781daccf642d2651a8f8"],"state_sha256":"1c8fcae0f1dd27381b6151e19b2542b0074e10c52e4172a04c0b013dcdd18e6e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5H8+5pGTqL7DMvM6FdlzDBpXMP/T4QF6VUYL0nNhM2HIvtdC7iXjBgUJeBfm8qOu/tZrirIt9lz58Xb1V0hmAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T22:45:55.470981Z","bundle_sha256":"4e83a75df230bf868c031ca7f336f73dbfdcb48bc2c03813543ce12f5346429b"}}