{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:OATGP5MMXYE76SY7O6AXL453YG","short_pith_number":"pith:OATGP5MM","schema_version":"1.0","canonical_sha256":"702667f58cbe09ff4b1f778175f3bbc1b5e065b824851a339911f8af6ef443ff","source":{"kind":"arxiv","id":"1504.04093","version":2},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1504.04093","created_at":"2026-05-18T01:11:25.382800+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.04093v2","created_at":"2026-05-18T01:11:25.382800+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.04093","created_at":"2026-05-18T01:11:25.382800+00:00"},{"alias_kind":"pith_short_12","alias_value":"OATGP5MMXYE7","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_16","alias_value":"OATGP5MMXYE76SY7","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_8","alias_value":"OATGP5MM","created_at":"2026-05-18T12:29:34.919912+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG","json":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG.json","graph_json":"https://pith.science/api/pith-number/OATGP5MMXYE76SY7O6AXL453YG/graph.json","events_json":"https://pith.science/api/pith-number/OATGP5MMXYE76SY7O6AXL453YG/events.json","paper":"https://pith.science/paper/OATGP5MM"},"agent_actions":{"view_html":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG","download_json":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG.json","view_paper":"https://pith.science/paper/OATGP5MM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.04093&json=true","fetch_graph":"https://pith.science/api/pith-number/OATGP5MMXYE76SY7O6AXL453YG/graph.json","fetch_events":"https://pith.science/api/pith-number/OATGP5MMXYE76SY7O6AXL453YG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/action/storage_attestation","attest_author":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/action/author_attestation","sign_citation":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/action/citation_signature","submit_replication":"https://pith.science/pith/OATGP5MMXYE76SY7O6AXL453YG/action/replication_record"}},"created_at":"2026-05-18T01:11:25.382800+00:00","updated_at":"2026-05-18T01:11:25.382800+00:00"}