- Bayesian Anal.
- Volume 11, Number 1 (2016), 191-213.
Computational Enhancements to Bayesian Design of Experiments Using Gaussian Processes
Bayesian design of experiments is a methodology for incorporating prior information into the design phase of an experiment. Unfortunately, the typical Bayesian approach to designing experiments is both numerically and analytically intractable without additional assumptions or approximations. In this paper, we discuss how Gaussian processes can be used to help alleviate the numerical issues associated with Bayesian design of experiments. We provide an example based on accelerated life tests and compare our results with large-sample methods.
Bayesian Anal., Volume 11, Number 1 (2016), 191-213.
First available in Project Euclid: 4 March 2015
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Weaver, Brian P.; Williams, Brian J.; Anderson-Cook, Christine M.; Higdon, David M. Computational Enhancements to Bayesian Design of Experiments Using Gaussian Processes. Bayesian Anal. 11 (2016), no. 1, 191--213. doi:10.1214/15-BA945. https://projecteuclid.org/euclid.ba/1425492493