The frontier of simulation-based inference

Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and...

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Veröffentlicht in:	Proceedings of the National Academy of Sciences - PNAS Jg. 117; H. 48; S. 30055
Hauptverfasser:	Cranmer, Kyle, Brehmer, Johann, Louppe, Gilles
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.12.2020
Schlagworte:	likelihood-free inference neural density estimation statistical inference approximate Bayesian computation implicit models
ISSN:	1091-6490, 1091-6490
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Beschreibung
Zusammenfassung:	Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving additional momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound influence these developments may have on science.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1091-6490 1091-6490
DOI:	10.1073/pnas.1912789117