Finding near-optimal Bayesian experimental designs via genetic algorithms

被引：61

作者：

Hamada, M ^{[1
]}

Martz, HF ^{[1
]}

Reese, CS ^{[1
]}

Wilson, AG ^{[1
]}

机构：

[1] Los Alamos Natl Lab, Los Alamos, NM 87545 USA

来源：

AMERICAN STATISTICIAN | 2001年 / 55卷 / 03期

关键词：

expected information gain; logistic regression; linear and nonlinear regression; multifactor designs; Shannon information;

D O I：

10.1198/000313001317098121

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article shows how a genetic algorithm can be used to find near-optimal Bayesian experimental 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.

引用

页码：175 / 181

页数：7

共 46 条

[1] Design of mixture experiments using Bayesian D-optimality [J].