| Literature DB >> 32471948 |
Kyle Cranmer1,2, Johann Brehmer3,2, Gilles Louppe4.
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
Year: 2020 PMID: 32471948 PMCID: PMC7720103 DOI: 10.1073/pnas.1912789117
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 11.205