{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:D722F4B6GRDTHPKZ6WCVU4JT5X","short_pith_number":"pith:D722F4B6","schema_version":"1.0","canonical_sha256":"1ff5a2f03e344733bd59f5855a7133edc0e5a0ab753133b53b4152dd284e77c3","source":{"kind":"arxiv","id":"1907.10940","version":1},"attestation_state":"computed","paper":{"title":"BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"stat.CO","authors_text":"Christopher Drovandi, Leah F South, Ziwen An","submitted_at":"2019-07-25T10:06:08Z","abstract_excerpt":"Bayesian synthetic likelihood (BSL) is a popular method for estimating the parameter posterior distribution for complex statistical models and stochastic processes that possess a computationally intractable likelihood function. Instead of evaluating the likelihood, BSL approximates the likelihood of a judiciously chosen summary statistic of the data via model simulation and density estimation. Compared to alternative methods such as approximate Bayesian computation (ABC), BSL requires little tuning and requires less model simulations than ABC when the chosen summary statistic is high-dimension"},"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":"1907.10940","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-07-25T10:06:08Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"fe1ffdfa37918ac2470df5c75fc141e14277caad30c0f2bde4c97219fbf1d774","abstract_canon_sha256":"5dc52c688ffe8da09b59859be5aef95bd39daa187bd5917b3eb1d427c422698f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:33.997452Z","signature_b64":"T/rHOcpsow7S9vjjhTi69L2f5Q82GqGtVbSVe16kbFTFsrxTTBh0x04lfLLn/dDuyvfDRQOlgTku80allTtqBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ff5a2f03e344733bd59f5855a7133edc0e5a0ab753133b53b4152dd284e77c3","last_reissued_at":"2026-05-17T23:39:33.997010Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:33.997010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"stat.CO","authors_text":"Christopher Drovandi, Leah F South, Ziwen An","submitted_at":"2019-07-25T10:06:08Z","abstract_excerpt":"Bayesian synthetic likelihood (BSL) is a popular method for estimating the parameter posterior distribution for complex statistical models and stochastic processes that possess a computationally intractable likelihood function. Instead of evaluating the likelihood, BSL approximates the likelihood of a judiciously chosen summary statistic of the data via model simulation and density estimation. Compared to alternative methods such as approximate Bayesian computation (ABC), BSL requires little tuning and requires less model simulations than ABC when the chosen summary statistic is high-dimension"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10940","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":"1907.10940","created_at":"2026-05-17T23:39:33.997061+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.10940v1","created_at":"2026-05-17T23:39:33.997061+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10940","created_at":"2026-05-17T23:39:33.997061+00:00"},{"alias_kind":"pith_short_12","alias_value":"D722F4B6GRDT","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"D722F4B6GRDTHPKZ","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"D722F4B6","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2104.03436","citing_title":"Synthetic likelihood in misspecified models","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X","json":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X.json","graph_json":"https://pith.science/api/pith-number/D722F4B6GRDTHPKZ6WCVU4JT5X/graph.json","events_json":"https://pith.science/api/pith-number/D722F4B6GRDTHPKZ6WCVU4JT5X/events.json","paper":"https://pith.science/paper/D722F4B6"},"agent_actions":{"view_html":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X","download_json":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X.json","view_paper":"https://pith.science/paper/D722F4B6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.10940&json=true","fetch_graph":"https://pith.science/api/pith-number/D722F4B6GRDTHPKZ6WCVU4JT5X/graph.json","fetch_events":"https://pith.science/api/pith-number/D722F4B6GRDTHPKZ6WCVU4JT5X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X/action/storage_attestation","attest_author":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X/action/author_attestation","sign_citation":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X/action/citation_signature","submit_replication":"https://pith.science/pith/D722F4B6GRDTHPKZ6WCVU4JT5X/action/replication_record"}},"created_at":"2026-05-17T23:39:33.997061+00:00","updated_at":"2026-05-17T23:39:33.997061+00:00"}