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arXiv:1911.01429stat.ML

The frontier of simulation-based inference

Kyle Cranmer, Johann Brehmer, Gilles Louppe

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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 new momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound change these developments may have on science.

arXiv comments
10 pages, 3 figures, proceedings for the Sackler Colloquia at the US National Academy of Sciences. v2: fixed typos. v3: clarified text, added references
Journal reference
DOI
doi:10.1073/pnas.1912789117
Subjects
stat.MLMachine Learning (Statistics) cs.LGMachine Learning stat.MEMethodology (Statistics)

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