{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:C5PTURBZPRDC36TBTBX35SJSWE","short_pith_number":"pith:C5PTURBZ","schema_version":"1.0","canonical_sha256":"175f3a44397c462dfa61986fbec932b1211620362c93211ab7752cc58eb751a5","source":{"kind":"arxiv","id":"2101.04653","version":2},"attestation_state":"computed","paper":{"title":"Benchmarking Simulation-Based Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"David S. Greenberg, Jakob H. Macke, Jan Boelts, Jan-Matthis Lueckmann, Pedro J. Gon\\c{c}alves","submitted_at":"2021-01-12T18:31:22Z","abstract_excerpt":"Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a public benchmark with appropriate performance metrics for such 'likelihood-free' algorithms has been lacking. This has made it difficult to compare algorithms and identify their strengths and weaknesses. We set out to fill this gap: We provide a benchmark with inference tasks and suitable performance metrics, with an initial selection of algorithms including recent approaches employing neural networks and classical A"},"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":"2101.04653","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-01-12T18:31:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ed4e00a2d187d9704ef5eda0d62d6663dc68ebd7daf698fa4a7ad6f0b764fffc","abstract_canon_sha256":"be3be70ba6a2e67c1a8b98b835c26b4db4b09ec281503a260094635cb72be224"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:30:31.343266Z","signature_b64":"MJOIkSshyMgMHx5xMj9aO9SMDPun5bZbuBaWUSivUimQBnWxTX58e6PwLOLWYpJqwryofsU4BeqBg0wIvKNrAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"175f3a44397c462dfa61986fbec932b1211620362c93211ab7752cc58eb751a5","last_reissued_at":"2026-07-05T02:30:31.342776Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:30:31.342776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking Simulation-Based Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"David S. Greenberg, Jakob H. Macke, Jan Boelts, Jan-Matthis Lueckmann, Pedro J. Gon\\c{c}alves","submitted_at":"2021-01-12T18:31:22Z","abstract_excerpt":"Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a public benchmark with appropriate performance metrics for such 'likelihood-free' algorithms has been lacking. This has made it difficult to compare algorithms and identify their strengths and weaknesses. We set out to fill this gap: We provide a benchmark with inference tasks and suitable performance metrics, with an initial selection of algorithms including recent approaches employing neural networks and classical A"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.04653","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2101.04653/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2101.04653","created_at":"2026-07-05T02:30:31.342840+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.04653v2","created_at":"2026-07-05T02:30:31.342840+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.04653","created_at":"2026-07-05T02:30:31.342840+00:00"},{"alias_kind":"pith_short_12","alias_value":"C5PTURBZPRDC","created_at":"2026-07-05T02:30:31.342840+00:00"},{"alias_kind":"pith_short_16","alias_value":"C5PTURBZPRDC36TB","created_at":"2026-07-05T02:30:31.342840+00:00"},{"alias_kind":"pith_short_8","alias_value":"C5PTURBZ","created_at":"2026-07-05T02:30:31.342840+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.27499","citing_title":"GenSBI: Generative Methods for Simulation-Based Inference in JAX","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24330","citing_title":"Pre-localization of Massive Black Hole Binaries in the Millihertz Band","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19919","citing_title":"Neural Simulation-based Inference with Hierarchical Priors for Detached Eclipsing Binaries","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE","json":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE.json","graph_json":"https://pith.science/api/pith-number/C5PTURBZPRDC36TBTBX35SJSWE/graph.json","events_json":"https://pith.science/api/pith-number/C5PTURBZPRDC36TBTBX35SJSWE/events.json","paper":"https://pith.science/paper/C5PTURBZ"},"agent_actions":{"view_html":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE","download_json":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE.json","view_paper":"https://pith.science/paper/C5PTURBZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.04653&json=true","fetch_graph":"https://pith.science/api/pith-number/C5PTURBZPRDC36TBTBX35SJSWE/graph.json","fetch_events":"https://pith.science/api/pith-number/C5PTURBZPRDC36TBTBX35SJSWE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE/action/storage_attestation","attest_author":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE/action/author_attestation","sign_citation":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE/action/citation_signature","submit_replication":"https://pith.science/pith/C5PTURBZPRDC36TBTBX35SJSWE/action/replication_record"}},"created_at":"2026-07-05T02:30:31.342840+00:00","updated_at":"2026-07-05T02:30:31.342840+00:00"}