Finding Near-Optimal Bayesian Experimental Designs via Genetic Algorithms
Finding Near-Optimal Bayesian Experimental Designs via Genetic Algorithms
复制标题
通过遗传算法寻找近乎最优的贝叶斯实验设计
DOI：
10.1198/000313001317098121
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Alyson G. Wilson
中科院分区：
文献类型：
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作者：
Michael S. Hamada;H. Martz;C. S. Reese;Alyson G. Wilson
这篇文章展示了如何使用遗传算法来寻找回归模型的接近最优的贝叶斯试验设计。所考虑的设计准则是后验分布与先验分布相比所获得的后验分布的预期香农信息增益。介绍了遗传算法，并将其应用于实验设计。然后用一系列的例子来说明这种方法：线性和非线性回归，单因素和多因素，以及正态分布和伯努利分布的实验数据。
This article shows how a genetic algorithm can be used to find near-optimal Bayesia nexperimental designs for regression models. The design criterion considered is the expected Shannon information gain of the posterior distribution obtained from performing a given experiment compared with the prior distribution. Genetic algorithms are described and then applied to experimental design. The methodology is then illustrated with a wide range of examples: linear and nonlinear regression, single and multiple factors, and normal and Bernoulli distributed experimental data.