GADGET package computes near-optimal Bayesian experimental designs
using Gaussian process optimization. At its core is the ability to
calculate static designs that maximize a design criterion that may be
either deterministic or stochastic. In particular, stochastic design
criteria could be a Monte Carlo estimator of an expected utility based
on MCMC posterior draws.
GADGET utilizes the algorithm proposed by
Weaver et al. (2016) and performs Gaussian process validation using the
statistics introduced by Bastos and O’Hagan (2009). The
package is integrated to parallelize the evaluation of the user’s design
GADGET has wrapped the optimization into a
sequential routine to perform sequential computer experiments that
automatically call simulator code that is available in R.
GADGET from github, use the
install_github function from
Bastos, L. S., & O’Hagan, A. (2009). Diagnostics for gaussian process emulators. Technometrics, 51(4), 425–438.
Weaver, B. P., Williams, B. J., Anderson-Cook, C. M., Higdon, D. M. (2016). Computational enhancements to Bayesian design of experiments using Gaussian processes. Bayesian Analysis, 11(1), 191–213.
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