acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange
Dublin Core
Title
acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange
Subject
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
nonlinear models, prediction, pseudo-Bayesian design
Description
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
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
Creator
Antony M. Overstall
Source
https://www.jstatsoft.org/article/view/v095i13
Publisher
University of Southampton
Date
October 2020
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Antony M. Overstall, “acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange,” Repository Horizon University Indonesia, accessed April 11, 2025, https://repository.horizon.ac.id/items/show/8163.