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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Vydáno v:Proceedings of the National Academy of Sciences - PNAS Ročník 117; číslo 48; s. 30055
Hlavní autoři: Cranmer, Kyle, Brehmer, Johann, Louppe, Gilles
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.12.2020
Témata:
likelihood-free inference
neural density estimation
statistical inference
approximate Bayesian computation
implicit models
ISSN:1091-6490, 1091-6490
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Shrnutí: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.
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ISSN:1091-6490
1091-6490
DOI:10.1073/pnas.1912789117