文章基本信息

标题：acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange
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作者：Antony M. Overstall ; David C. Woods ; Maria Adamou 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2020
卷号：95
期号：1
页码：1-33
DOI：10.18637/jss.v095.i13
出版社：University of California, Los Angeles
摘要：We describe the R package acebayes and demonstrate its use to find Bayesian optimal experimental designs. A decision-theoretic approach is adopted, with the optimal design maximizing an expected utility. Finding Bayesian optimal designs for realistic problems is challenging, as the expected utility is typically intractable and the design space may be high-dimensional. The package implements the approximate coordinate exchange algorithm to optimize (an approximation to) the expected utility via a sequence of conditional one-dimensional optimization steps. At each step, a Gaussian process regression model is used to approximate, and subsequently optimize, the expected utility as the function of a single design coordinate (the value taken by one controllable variable for one run of the experiment). In addition to functions for bespoke design problems with user-defined utility functions, acebayes provides functions tailored to finding designs for common generalized linear and nonlinear models. The package provides a step-change in the complexity of problems that can be addressed, enabling designs to be found for much larger numbers of variables and runs than previously possible. We provide tutorials on the application of the methodology for four illustrative examples of varying complexity where designs are found for the goals of parameter estimation, model selection and prediction. These examples demonstrate previously unseen functionality of acebayes.
关键词：A-optimality;computer experiments;D-optimality;decision-theoretic design;Gaussian process regression;generalized linear models;high-dimensional design;model selection;nonlinear models;prediction;pseudo-Bayesian design.
其他关键词：A-optimality;computer experiments;D-optimality;decision-theoretic design;Gaussian process regression;generalized linear models;high-dimensional design;model selection;nonlinear models;prediction;pseudo-Bayesian design