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  • 标题:The frontier of simulation-based inference
  • 本地全文:下载
  • 作者:Kyle Cranmer ; Johann Brehmer ; Gilles Louppe
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2020
  • 卷号:117
  • 期号:48
  • 页码:30055-30062
  • DOI:10.1073/pnas.1912789117
  • 出版社:The National Academy of Sciences of the United States of America
  • 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.
  • 关键词:statistical inference ; implicit models ; likelihood-free inference ; approximate Bayesian computation ; neural density estimation
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