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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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS Vol. 117; no. 48; p. 30055
Main Authors: Cranmer, Kyle, Brehmer, Johann, Louppe, Gilles
Format: Journal Article
Language:English
Published: United States 01.12.2020
Subjects:
likelihood-free inference
neural density estimation
statistical inference
approximate Bayesian computation
implicit models
ISSN:1091-6490, 1091-6490
Online Access:Get more information
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Summary: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