实验设计
多样性(控制论)
计算机科学
过程(计算)
统计模型
数学模型
统计分析
数学优化
数学
机器学习
统计
人工智能
操作系统
标识
DOI:10.1002/cite.201800100
摘要
Abstract Design of experiments (DoE) is a family of methods for performing experiments that are maximally informative for a chosen mathematical model. Statistical design of experiments focuses on empirical models that are sufficiently flexible as to describe a wide variety of systems, while having favorable mathematical properties for convenient estimation and optimization. This review describes approaches in statistical DoE for screening through many potential influencing factors and finding optimum process conditions.
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