acebayes

acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange. We describe and demonstrate the use of the R package acebayes to find Bayesian optimal experimental designs. A decision-theoretic approach is adopted, with the optimal design maximising 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 (ACE) algorithm to optimise (an approximation to) the expected utility via a sequence of conditional one-dimensional optimisation steps. At each step, a Gaussian process regression model is used to approximate, and subsequently optimise, the expected utility as the function of a single design coordinate (the value taken by one controllable variable for one run of the experiment). Functions are provided for both bespoke design problems with user-defined utility functions and for common generalised 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 illustrate the methodology on four examples of varying complexity where designs are found for the goals of parameter estimation, model selection and prediction.


References in zbMATH (referenced in 11 articles )

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  1. Overstall, Antony; Mcgree, James: Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation (2020)
  2. Overstall, Antony M.; Woods, David C.; Parker, Ben M.: Bayesian optimal design for ordinary differential equation models with application in biological science (2020)
  3. Senarathne, S. G. J.; Drovandi, C. C.; McGree, J. M.: A Laplace-based algorithm for Bayesian adaptive design (2020)
  4. Senarathne, S. G. J.; Drovandi, C. C.; McGree, J. M.: Bayesian sequential design for copula models (2020)
  5. Alan R. Pearse, James M. McGree, Nicholas A. Som, Catherine Leigh, Jay M. Ver Hoef, Paul Maxwell, Erin E. Peterson: SSNdesign - an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks (2019) arXiv
  6. Gillespie, C. S.; Boys, R. J.: Efficient construction of Bayes optimal designs for stochastic process models (2019)
  7. Overstall, Antony M.; Woods, David C.; Martin, Kieran J.: Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics (2019)
  8. Dehideniya, Mahasen B.; Drovandi, Christopher C.; McGree, James M.: Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology (2018)
  9. Overstall, Antony M.; McGree, James M.; Drovandi, Christopher C.: An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions (2018)
  10. Price, David J.; Bean, Nigel G.; Ross, Joshua V.; Tuke, Jonathan: An induced natural selection heuristic for finding optimal Bayesian experimental designs (2018)
  11. Antony Overstall, David Woods, Maria Adamou: acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange (2017) arXiv