期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 117, 期 48, 页码 30055-30062出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1912789117
关键词
statistical inference; implicit models; likelihood-free inference; approximate Bayesian computation; neural density estimation
资金
- National Science Foundation [ACI-1450310, OAC-1836650, OAC-1841471]
- Moore-Sloan data science environment at New York University
- University of Li `ege-Network Research Belgium (NRB)
- NRB
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.
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