4.8 Article

The frontier of simulation-based inference

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

  1. National Science Foundation [ACI-1450310, OAC-1836650, OAC-1841471]
  2. Moore-Sloan data science environment at New York University
  3. University of Li `ege-Network Research Belgium (NRB)
  4. NRB

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据