{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:OATGP5MMXYE76SY7O6AXL453YG","short_pith_number":"pith:OATGP5MM","canonical_record":{"source":{"id":"1504.04093","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-04-16T03:33:13Z","cross_cats_sorted":[],"title_canon_sha256":"54e7b6f5fa448d93f549f2245386caf4e0d8ce3e7a5d964b7399086c3733fedc","abstract_canon_sha256":"ba19a31177a10fd5c1151048bf3e803e53a4f0e0d458f65e66d22288364fe902"},"schema_version":"1.0"},"canonical_sha256":"702667f58cbe09ff4b1f778175f3bbc1b5e065b824851a339911f8af6ef443ff","source":{"kind":"arxiv","id":"1504.04093","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.04093","created_at":"2026-05-18T01:11:25Z"},{"alias_kind":"arxiv_version","alias_value":"1504.04093v2","created_at":"2026-05-18T01:11:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.04093","created_at":"2026-05-18T01:11:25Z"},{"alias_kind":"pith_short_12","alias_value":"OATGP5MMXYE7","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"OATGP5MMXYE76SY7","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"OATGP5MM","created_at":"2026-05-18T12:29:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:OATGP5MMXYE76SY7O6AXL453YG","target":"record","payload":{"canonical_record":{"source":{"id":"1504.04093","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-04-16T03:33:13Z","cross_cats_sorted":[],"title_canon_sha256":"54e7b6f5fa448d93f549f2245386caf4e0d8ce3e7a5d964b7399086c3733fedc","abstract_canon_sha256":"ba19a31177a10fd5c1151048bf3e803e53a4f0e0d458f65e66d22288364fe902"},"schema_version":"1.0"},"canonical_sha256":"702667f58cbe09ff4b1f778175f3bbc1b5e065b824851a339911f8af6ef443ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:11:25.383214Z","signature_b64":"RcDR6NsMgVZpjgjWAEXA+lDzo2+uEIXKDiM4Sv1nH6lJzxS+78oFPatBZAK3CLqYiEfrnnMUCA9Tn4FaN38/AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"702667f58cbe09ff4b1f778175f3bbc1b5e065b824851a339911f8af6ef443ff","last_reissued_at":"2026-05-18T01:11:25.382726Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:11:25.382726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1504.04093","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":"2026-05-18T01:11:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Khyv8f6hlLVtMrzuoLmnsM1KZLx28nU1Ckr8hSYHpkVtPD/YAk1GWsa5s0V/2P9kr0WNUjhntB7MM5cBY0pZBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T23:14:46.899308Z"},"content_sha256":"84ea30768e31ec8c1753bd73d6528f43de34be83aa6892c96c49b7b2d9e81fbe","schema_version":"1.0","event_id":"sha256:84ea30768e31ec8c1753bd73d6528f43de34be83aa6892c96c49b7b2d9e81fbe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:OATGP5MMXYE76SY7O6AXL453YG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"David J. Nott, Jingjing Li, Scott A. Sisson, Yanan Fan","submitted_at":"2015-04-16T03:33:13Z","abstract_excerpt":"Approximate Bayesian computation (ABC) refers to a family of inference methods used in the Bayesian analysis of complex models where evaluation of the likelihood is difficult. Conventional ABC methods often suffer from the curse of dimensionality, and a marginal adjustment strategy was recently introduced in the literature to improve the performance of ABC algorithms in high-dimensional problems. The marginal adjustment approach is extended using a Gaussian copula approximation. The method first estimates the bivariate posterior for each pair of parameters separately using a 2-dimensional Gaus"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.04093","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":"2026-05-18T01:11:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nk3SSThWPZjtYiX+5BEURZWbfMRLEBos4j/XTaq+ZtOdFYx8WQEgUl7UnCKUpHf2bkCX4WXmyo0gZRgURh9pCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T23:14:46.900070Z"},"content_sha256":"61da05ea0e36b225540b27a1c99f63e7b439f25a7fed043bc398f03f783fdf51","schema_version":"1.0","event_id":"sha256:61da05ea0e36b225540b27a1c99f63e7b439f25a7fed043bc398f03f783fdf51"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/bundle.json","state_url":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OATGP5MMXYE76SY7O6AXL453YG/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":"2026-05-30T23:14:46Z","links":{"resolver":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG","bundle":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/bundle.json","state":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OATGP5MMXYE76SY7O6AXL453YG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:OATGP5MMXYE76SY7O6AXL453YG","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":"ba19a31177a10fd5c1151048bf3e803e53a4f0e0d458f65e66d22288364fe902","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-04-16T03:33:13Z","title_canon_sha256":"54e7b6f5fa448d93f549f2245386caf4e0d8ce3e7a5d964b7399086c3733fedc"},"schema_version":"1.0","source":{"id":"1504.04093","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.04093","created_at":"2026-05-18T01:11:25Z"},{"alias_kind":"arxiv_version","alias_value":"1504.04093v2","created_at":"2026-05-18T01:11:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.04093","created_at":"2026-05-18T01:11:25Z"},{"alias_kind":"pith_short_12","alias_value":"OATGP5MMXYE7","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"OATGP5MMXYE76SY7","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"OATGP5MM","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:61da05ea0e36b225540b27a1c99f63e7b439f25a7fed043bc398f03f783fdf51","target":"graph","created_at":"2026-05-18T01:11:25Z","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":"Approximate Bayesian computation (ABC) refers to a family of inference methods used in the Bayesian analysis of complex models where evaluation of the likelihood is difficult. Conventional ABC methods often suffer from the curse of dimensionality, and a marginal adjustment strategy was recently introduced in the literature to improve the performance of ABC algorithms in high-dimensional problems. The marginal adjustment approach is extended using a Gaussian copula approximation. The method first estimates the bivariate posterior for each pair of parameters separately using a 2-dimensional Gaus","authors_text":"David J. Nott, Jingjing Li, Scott A. Sisson, Yanan Fan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-04-16T03:33:13Z","title":"Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.04093","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":"sha256:84ea30768e31ec8c1753bd73d6528f43de34be83aa6892c96c49b7b2d9e81fbe","target":"record","created_at":"2026-05-18T01:11:25Z","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":"ba19a31177a10fd5c1151048bf3e803e53a4f0e0d458f65e66d22288364fe902","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-04-16T03:33:13Z","title_canon_sha256":"54e7b6f5fa448d93f549f2245386caf4e0d8ce3e7a5d964b7399086c3733fedc"},"schema_version":"1.0","source":{"id":"1504.04093","kind":"arxiv","version":2}},"canonical_sha256":"702667f58cbe09ff4b1f778175f3bbc1b5e065b824851a339911f8af6ef443ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"702667f58cbe09ff4b1f778175f3bbc1b5e065b824851a339911f8af6ef443ff","first_computed_at":"2026-05-18T01:11:25.382726Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:11:25.382726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RcDR6NsMgVZpjgjWAEXA+lDzo2+uEIXKDiM4Sv1nH6lJzxS+78oFPatBZAK3CLqYiEfrnnMUCA9Tn4FaN38/AA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:11:25.383214Z","signed_message":"canonical_sha256_bytes"},"source_id":"1504.04093","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84ea30768e31ec8c1753bd73d6528f43de34be83aa6892c96c49b7b2d9e81fbe","sha256:61da05ea0e36b225540b27a1c99f63e7b439f25a7fed043bc398f03f783fdf51"],"state_sha256":"db0065b15f883a6e56666a7489a21f18b63104163c9c88b92e4ad39cad91c976"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UrXhrc4fkKgfOL8ClEmjiBTNH6c+dYLhVjfiHl2CvqOlCsNOJ+mW0i2QPFIu6ooSpo6Bny0cE+pVyKhdiQHiBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T23:14:46.903837Z","bundle_sha256":"2ec40d63926e9cc8227eff870281d487677013aec6a08bc6bbe2a5d8c20839be"}}