The frontier of simulation-based inference
Machine Learning
2022-06-08 v3 Machine Learning
Methodology
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.
Cite
@article{arxiv.1911.01429,
title = {The frontier of simulation-based inference},
author = {Kyle Cranmer and Johann Brehmer and Gilles Louppe},
journal= {arXiv preprint arXiv:1911.01429},
year = {2022}
}
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