Bayesian Analysis

Computational Enhancements to Bayesian Design of Experiments Using Gaussian Processes

Brian P. Weaver, Brian J. Williams, Christine M. Anderson-Cook, and David M. Higdon

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Abstract

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.

Article information

Source
Bayesian Anal., Volume 11, Number 1 (2016), 191-213.

Dates
First available in Project Euclid: 4 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.ba/1425492493

Digital Object Identifier
doi:10.1214/15-BA945

Mathematical Reviews number (MathSciNet)
MR3447096

Zentralblatt MATH identifier
1359.62322

Keywords
Bayesian design of experiments Gaussian processes accelerated life tests preposterior expectation expected quantile improvement

Citation

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


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