{"paper":{"title":"SBI -- A toolkit for simulation-based inference","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["q-bio.QM","stat.CO","stat.ML"],"primary_cat":"cs.LG","authors_text":"(2) School of Informatics, 3), (3) Neural Systems Analysis, 4), (4) Model-Driven Machine Learning, 5, (5) Machine Learning in Science, 6) ((1) Computational Neuroengineering, (6) Empirical Inference, Alvaro Tejero-Cantero (1), Bonn, Center of Advanced European Studies, Centre for Materials, Coastal Research, Computer Engineering, Conor Durkan (2), David S. Greenberg (1, Department of Electrical, Helmholtz-Zentrum Geesthacht, Jakob H. Macke (1, Jan Boelts (1), Jan-Matthis Lueckmann (1), Max Planck Institute for Intelligent Systems, Michael Deistler (1), Pedro J. Gon\\c{c}alves (1, Research (caesar), Technical University of Munich, T\\\"ubingen), University of Edinburgh, University of T\\\"ubingen","submitted_at":"2020-07-17T16:53:51Z","abstract_excerpt":"Scientists and engineers employ stochastic numerical simulators to model empirically observed phenomena. In contrast to purely statistical models, simulators express scientific principles that provide powerful inductive biases, improve generalization to new data or scenarios and allow for fewer, more interpretable and domain-relevant parameters. Despite these advantages, tuning a simulator's parameters so that its outputs match data is challenging. Simulation-based inference (SBI) seeks to identify parameter sets that a) are compatible with prior knowledge and b) match empirical observations. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.09114","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/2007.09114/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"}