{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:755QJHNU6MG32O26FQHAG4A6OP","short_pith_number":"pith:755QJHNU","schema_version":"1.0","canonical_sha256":"ff7b049db4f30dbd3b5e2c0e03701e73d7aecb046bcd42b04c2c41bc5c054665","source":{"kind":"arxiv","id":"1301.6282","version":1},"attestation_state":"computed","paper":{"title":"AABC: approximate approximate Bayesian computation when simulating a large number of data sets is computationally infeasible","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Erkan O. Buzbas, Noah A. Rosenberg","submitted_at":"2013-01-26T19:31:26Z","abstract_excerpt":"Approximate Bayesian computation (ABC) methods perform inference on model-specific parameters of mechanistically motivated parametric statistical models when evaluating likelihoods is difficult. Central to the success of ABC methods is computationally inexpensive simulation of data sets from the parametric model of interest. However, when simulating data sets from a model is so computationally expensive that the posterior distribution of parameters cannot be adequately sampled by ABC, inference is not straightforward. We present approximate approximate Bayesian computation\" (AABC), a class of "},"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":"1301.6282","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2013-01-26T19:31:26Z","cross_cats_sorted":[],"title_canon_sha256":"51aae3af2b8001ca4dea3879adaec5bb015d19c3aa9cef4581a92fab4f027c5e","abstract_canon_sha256":"ebe9ffc3d41458d02a86e8dd034d49c463f3879e08e64ea91a39a20382b4d462"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:35:15.928211Z","signature_b64":"Jui1UXdWtFb2Gpk/bd5a9/ymfIgeJvIT5BD2/vh+ucx6QiqHcxhIeTG+9rTmjwE9ynMRQ9ly8trzXFLx8Ia3BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff7b049db4f30dbd3b5e2c0e03701e73d7aecb046bcd42b04c2c41bc5c054665","last_reissued_at":"2026-05-18T03:35:15.927472Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:35:15.927472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AABC: approximate approximate Bayesian computation when simulating a large number of data sets is computationally infeasible","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Erkan O. Buzbas, Noah A. Rosenberg","submitted_at":"2013-01-26T19:31:26Z","abstract_excerpt":"Approximate Bayesian computation (ABC) methods perform inference on model-specific parameters of mechanistically motivated parametric statistical models when evaluating likelihoods is difficult. Central to the success of ABC methods is computationally inexpensive simulation of data sets from the parametric model of interest. However, when simulating data sets from a model is so computationally expensive that the posterior distribution of parameters cannot be adequately sampled by ABC, inference is not straightforward. We present approximate approximate Bayesian computation\" (AABC), a class of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.6282","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1301.6282","created_at":"2026-05-18T03:35:15.927609+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.6282v1","created_at":"2026-05-18T03:35:15.927609+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.6282","created_at":"2026-05-18T03:35:15.927609+00:00"},{"alias_kind":"pith_short_12","alias_value":"755QJHNU6MG3","created_at":"2026-05-18T12:27:36.564083+00:00"},{"alias_kind":"pith_short_16","alias_value":"755QJHNU6MG32O26","created_at":"2026-05-18T12:27:36.564083+00:00"},{"alias_kind":"pith_short_8","alias_value":"755QJHNU","created_at":"2026-05-18T12:27:36.564083+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/755QJHNU6MG32O26FQHAG4A6OP","json":"https://pith.science/pith/755QJHNU6MG32O26FQHAG4A6OP.json","graph_json":"https://pith.science/api/pith-number/755QJHNU6MG32O26FQHAG4A6OP/graph.json","events_json":"https://pith.science/api/pith-number/755QJHNU6MG32O26FQHAG4A6OP/events.json","paper":"https://pith.science/paper/755QJHNU"},"agent_actions":{"view_html":"https://pith.science/pith/755QJHNU6MG32O26FQHAG4A6OP","download_json":"https://pith.science/pith/755QJHNU6MG32O26FQHAG4A6OP.json","view_paper":"https://pith.science/paper/755QJHNU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.6282&json=true","fetch_graph":"https://pith.science/api/pith-number/755QJHNU6MG32O26FQHAG4A6OP/graph.json","fetch_events":"https://pith.science/api/pith-number/755QJHNU6MG32O26FQHAG4A6OP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/755QJHNU6MG32O26FQHAG4A6OP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/755QJHNU6MG32O26FQHAG4A6OP/action/storage_attestation","attest_author":"https://pith.science/pith/755QJHNU6MG32O26FQHAG4A6OP/action/author_attestation","sign_citation":"https://pith.science/pith/755QJHNU6MG32O26FQHAG4A6OP/action/citation_signature","submit_replication":"https://pith.science/pith/755QJHNU6MG32O26FQHAG4A6OP/action/replication_record"}},"created_at":"2026-05-18T03:35:15.927609+00:00","updated_at":"2026-05-18T03:35:15.927609+00:00"}