The frontier of simulation-based inference

被引：523

作者：

Cranmer, Kyle ^{[1
,2
]}

Brehmer, Johann ^{[1
,2
]}

Louppe, Gilles ^{[3
]}

机构：

[1] NYU, Ctr Cosmol & Particle Phys, New York, NY 10003 USA

[2] NYU, Ctr Data Sci, New York, NY 10011 USA

[3] Univ Liege, Montefiore Inst, B-4000 Liege, Belgium

来源：

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 2020年 / 117卷 / 48期

基金：

美国国家科学基金会;

关键词：

statistical inference; implicit models; likelihood-free inference; approximate Bayesian computation; neural density estimation; APPROXIMATE BAYESIAN COMPUTATION; SEQUENTIAL MONTE-CARLO;

D O I：

10.1073/pnas.1912789117

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：30055 / 30062

页数：8