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. 2020 Dec 1;117(48):30055-30062.
doi: 10.1073/pnas.1912789117. Epub 2020 May 29.

The frontier of simulation-based inference

Kyle Cranmer  1   2 , Johann Brehmer  3   2 , Gilles Louppe  4
Affiliations
Free PMC article

The frontier of simulation-based inference

Kyle Cranmer et al. Proc Natl Acad Sci U S A. .
Free PMC article

Abstract

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.

Keywords: approximate Bayesian computation; implicit models; likelihood-free inference; neural density estimation; statistical inference.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
(A–H) Overview of different approaches to simulation-based inference.
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