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.
Funder
National Science Foundation
Gordon and Betty Moore Foundation
Alfred P. Sloan Foundation
Publisher
Proceedings of the National Academy of Sciences
Reference78 articles.
1. Monte Carlo methods of inference for implicit statistical models;Diggle;J. R. Stat. Soc. Ser. B,1984
2. Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician
3. Approximate Bayesian computation in population genetics;Beaumont;Genetics,2002
4. S. Mohamed , B. Lakshminarayanan , Learning in implicit generative models. arXiv:1610.03483 (11 October 2016).
5. S. A. Sisson , Y. Fan , M. Beaumont , Handbook of Approximate Bayesian Computation (Chapman and Hall/CRC, 2018).
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