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A Metaheuristic Adaptive Cubature Based Algorithm to Find Bayesian Optimal Designs for Nonlinear Models

机译:基于贝叶斯的非线性模型的贝叶斯最优设计的沟养自适应型号

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摘要

Finding Bayesian optimal designs for nonlinear models is a difficult task because the optimality criterion typically requires us to evaluate complex integrals before we perform a constrained optimization. We propose a hybridized method where we combine an adaptive multidimensional integration algorithm and a metaheuristic algorithm called imperialist competitive algorithm to find Bayesian optimal designs. We apply our numerical method to a few challenging design problems to demonstrate its efficiency. They include finding D-optimal designs for an item response model commonly used in education, Bayesian optimal designs for survival models, and Bayesian optimal designs for a four-parameter sigmoid Emax dose response model. for this article are available online and they contain an R package for implementing the proposed algorithm and codes for reproducing all the results in this article.
机译:寻找非线性模型的贝叶斯最优设计是一项艰巨的任务,因为最优标准通常要求我们在执行约束优化之前评估复杂积分。 我们提出了一种杂交的方法,在那里,我们将自适应多维集成算法和一种称为帝国主义竞争算法的成群质算法结合起来寻找贝叶斯型优化设计。 我们将数字方法应用于一些具有挑战性的设计问题,以证明其效率。 它们包括为常用于教育的项目响应模型,贝叶斯的最佳设计,为生存模型和四个参数Sigmoid Emax剂量响应模型的贝叶斯最优设计找到D-OPTEMAL设计。 对于本文可在线提供,它们包含用于实现所提出的算法和代码的R包,用于再现本文中的所有结果。

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