Using Bayesian parameter estimation to learn more from data without black boxes

被引：0

作者：

Rachel C. Kurchin

机构：

[1] Carnegie Mellon University,

[2] Materials Science and Engineering Department,undefined

来源：

Nature Reviews Physics | 2024年 / 6卷

关键词：

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.

引用

页码：152 / 154

页数：2

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