Hamada, M.; Martz, H. F.; Reese, C. S.; Wilson, A. G. Finding near-optimal Bayesian experimental designs via genetic algorithms. (English) Zbl 1182.62156 Am. Stat. 55, No. 3, 175-181 (2001). Summary: 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 designs. 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. Cited in 14 Documents MSC: 62K05 Optimal statistical designs 62C10 Bayesian problems; characterization of Bayes procedures 62J12 Generalized linear models (logistic models) 62B10 Statistical aspects of information-theoretic topics 90C59 Approximation methods and heuristics in mathematical programming 62F15 Bayesian inference Keywords:expected information gain; logistic regression; linear and nonlinear regression; multifactor designs; Shannon information Software:tsbridge PDF BibTeX XML Cite \textit{M. Hamada} et al., Am. Stat. 55, No. 3, 175--181 (2001; Zbl 1182.62156) Full Text: DOI OpenURL