{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:QKZDFYZV34EQXJMJKOWB6DAS7X","short_pith_number":"pith:QKZDFYZV","canonical_record":{"source":{"id":"1808.06040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-08-18T04:52:37Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"af5af2fb0d763d101f9e3d68155600417036a7924348d26c8810135060838a83","abstract_canon_sha256":"15d17b2b6c6db1c520e6627e8cd63065e4f97410dbfa736f642cc57173def38f"},"schema_version":"1.0"},"canonical_sha256":"82b232e335df090ba58953ac1f0c12fdd0996f3bb4924ba416ea4e65a66d1181","source":{"kind":"arxiv","id":"1808.06040","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.06040","created_at":"2026-05-18T00:07:46Z"},{"alias_kind":"arxiv_version","alias_value":"1808.06040v1","created_at":"2026-05-18T00:07:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06040","created_at":"2026-05-18T00:07:46Z"},{"alias_kind":"pith_short_12","alias_value":"QKZDFYZV34EQ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QKZDFYZV34EQXJMJ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QKZDFYZV","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:QKZDFYZV34EQXJMJKOWB6DAS7X","target":"record","payload":{"canonical_record":{"source":{"id":"1808.06040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-08-18T04:52:37Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"af5af2fb0d763d101f9e3d68155600417036a7924348d26c8810135060838a83","abstract_canon_sha256":"15d17b2b6c6db1c520e6627e8cd63065e4f97410dbfa736f642cc57173def38f"},"schema_version":"1.0"},"canonical_sha256":"82b232e335df090ba58953ac1f0c12fdd0996f3bb4924ba416ea4e65a66d1181","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:46.340131Z","signature_b64":"M07CGcMfRr6yLyituCaYPf/TBZ0YcFA4AxJ0ayXZeVE9VLZGa6q6KmDEer0EZsGsk+fx744CSxC87JDmBTIhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82b232e335df090ba58953ac1f0c12fdd0996f3bb4924ba416ea4e65a66d1181","last_reissued_at":"2026-05-18T00:07:46.339542Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:46.339542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.06040","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":"2026-05-18T00:07:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F/gDaRY+ZCkYxc8w3b3QKLvHfECIl2dTkp/ZEUtcLWxJ0zmV3hjslZE7zdS+e4rJltx0ZneLh5GU1vXGdh51Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T04:16:51.347064Z"},"content_sha256":"ecd4ec32f251ce180a8d585be8d3ba07791446e00ad55f2c7ae5cc4ac3dacab7","schema_version":"1.0","event_id":"sha256:ecd4ec32f251ce180a8d585be8d3ba07791446e00ad55f2c7ae5cc4ac3dacab7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:QKZDFYZV34EQXJMJKOWB6DAS7X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Optimal proposals for Approximate Bayesian Computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Benjamin D. Wandelt, Justin Alsing, Stephen M. Feeney","submitted_at":"2018-08-18T04:52:37Z","abstract_excerpt":"We derive the optimal proposal density for Approximate Bayesian Computation (ABC) using Sequential Monte Carlo (SMC) (or Population Monte Carlo, PMC). The criterion for optimality is that the SMC/PMC-ABC sampler maximise the effective number of samples per parameter proposal. The optimal proposal density represents the optimal trade-off between favoring high acceptance rate and reducing the variance of the importance weights of accepted samples. We discuss two convenient approximations of this proposal and show that the optimal proposal density gives a significant boost in the expected samplin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06040","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":"2026-05-18T00:07:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sEC6yQNxIc47/b26eVLYJk3XudYlUVmvoZbMqSNkdhDERoAL7JP22jFbglIgl51+ZbkiIq5MAhx7/jbQ210NCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T04:16:51.347732Z"},"content_sha256":"567f5b4768a2ff78337beb142d4e0146e5d83435f8a9aa039e0738db5cc5ccd4","schema_version":"1.0","event_id":"sha256:567f5b4768a2ff78337beb142d4e0146e5d83435f8a9aa039e0738db5cc5ccd4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QKZDFYZV34EQXJMJKOWB6DAS7X/bundle.json","state_url":"https://pith.science/pith/QKZDFYZV34EQXJMJKOWB6DAS7X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QKZDFYZV34EQXJMJKOWB6DAS7X/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":"2026-05-30T04:16:51Z","links":{"resolver":"https://pith.science/pith/QKZDFYZV34EQXJMJKOWB6DAS7X","bundle":"https://pith.science/pith/QKZDFYZV34EQXJMJKOWB6DAS7X/bundle.json","state":"https://pith.science/pith/QKZDFYZV34EQXJMJKOWB6DAS7X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QKZDFYZV34EQXJMJKOWB6DAS7X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QKZDFYZV34EQXJMJKOWB6DAS7X","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":"15d17b2b6c6db1c520e6627e8cd63065e4f97410dbfa736f642cc57173def38f","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-08-18T04:52:37Z","title_canon_sha256":"af5af2fb0d763d101f9e3d68155600417036a7924348d26c8810135060838a83"},"schema_version":"1.0","source":{"id":"1808.06040","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.06040","created_at":"2026-05-18T00:07:46Z"},{"alias_kind":"arxiv_version","alias_value":"1808.06040v1","created_at":"2026-05-18T00:07:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06040","created_at":"2026-05-18T00:07:46Z"},{"alias_kind":"pith_short_12","alias_value":"QKZDFYZV34EQ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QKZDFYZV34EQXJMJ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QKZDFYZV","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:567f5b4768a2ff78337beb142d4e0146e5d83435f8a9aa039e0738db5cc5ccd4","target":"graph","created_at":"2026-05-18T00:07:46Z","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":"We derive the optimal proposal density for Approximate Bayesian Computation (ABC) using Sequential Monte Carlo (SMC) (or Population Monte Carlo, PMC). The criterion for optimality is that the SMC/PMC-ABC sampler maximise the effective number of samples per parameter proposal. The optimal proposal density represents the optimal trade-off between favoring high acceptance rate and reducing the variance of the importance weights of accepted samples. We discuss two convenient approximations of this proposal and show that the optimal proposal density gives a significant boost in the expected samplin","authors_text":"Benjamin D. Wandelt, Justin Alsing, Stephen M. Feeney","cross_cats":["stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-08-18T04:52:37Z","title":"Optimal proposals for Approximate Bayesian Computation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06040","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:ecd4ec32f251ce180a8d585be8d3ba07791446e00ad55f2c7ae5cc4ac3dacab7","target":"record","created_at":"2026-05-18T00:07:46Z","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":"15d17b2b6c6db1c520e6627e8cd63065e4f97410dbfa736f642cc57173def38f","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-08-18T04:52:37Z","title_canon_sha256":"af5af2fb0d763d101f9e3d68155600417036a7924348d26c8810135060838a83"},"schema_version":"1.0","source":{"id":"1808.06040","kind":"arxiv","version":1}},"canonical_sha256":"82b232e335df090ba58953ac1f0c12fdd0996f3bb4924ba416ea4e65a66d1181","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"82b232e335df090ba58953ac1f0c12fdd0996f3bb4924ba416ea4e65a66d1181","first_computed_at":"2026-05-18T00:07:46.339542Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:46.339542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M07CGcMfRr6yLyituCaYPf/TBZ0YcFA4AxJ0ayXZeVE9VLZGa6q6KmDEer0EZsGsk+fx744CSxC87JDmBTIhCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:46.340131Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.06040","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ecd4ec32f251ce180a8d585be8d3ba07791446e00ad55f2c7ae5cc4ac3dacab7","sha256:567f5b4768a2ff78337beb142d4e0146e5d83435f8a9aa039e0738db5cc5ccd4"],"state_sha256":"658239fe2221903b9d4261f75d72a8f75d49d48c2771dbf579edc2662c95cd0e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"irer+vag6pM0yKHAPhFZ/Sxhm2EE82PtiW79trausEhdnjo8+433FGHmO/+mHiUtoLIZLQWUSjTrQ92+p9BbAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T04:16:51.350601Z","bundle_sha256":"92d867338cc723e22b3971ea1cf3193abb7fa12c8548c66b137ecf61b4fbc0ca"}}