The frontier of simulation-based inference

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 117; no. 48; pp. 30055 - 30062
• Main Authors: Cranmer, Kyle, Brehmer, Johann, Louppe, Gilles
• Format: Journal Article Web Resource
• Language: English
• Published: United States National Academy of Sciences 01.12.2020
• Subjects: Approximate Bayesian Computation; COLLOQUIUM PAPERS; Computer science; Engineering, computing & technology; Implicit models; Inference; Ingénierie, informatique & technologie; Inverse problems; Likelihood-free inference; Neural density estimation; on the Science of Deep Learning; Physical Sciences; Physical, chemical, mathematical & earth Sciences; Physics; Physique; Physique, chimie, mathématiques & sciences de la terre; Sciences informatiques; Simulation; Statistical inference; likelihood-free inference; neural density estimation; statistical inference; approximate Bayesian computation; implicit models
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1912789117

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.