Translator Disclaimer
March 2018 Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo
Christopher C. Drovandi, Minh-Ngoc Tran
Bayesian Anal. 13(1): 139-162 (March 2018). DOI: 10.1214/16-BA1045

Abstract

Optimal experimental design is an important methodology for most efficiently allocating resources in an experiment to best achieve some goal. Bayesian experimental design considers the potential impact that various choices of the controllable variables have on the posterior distribution of the unknowns. Optimal Bayesian design involves maximising an expected utility function, which is an analytically intractable integral over the prior predictive distribution. These integrals are typically estimated via standard Monte Carlo methods. In this paper, we demonstrate that the use of randomised quasi-Monte Carlo can bring significant reductions to the variance of the estimated expected utility. This variance reduction can then lead to a more efficient optimisation algorithm for maximising the expected utility.

Citation

Download Citation

Christopher C. Drovandi. Minh-Ngoc Tran. "Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo." Bayesian Anal. 13 (1) 139 - 162, March 2018. https://doi.org/10.1214/16-BA1045

Information

Published: March 2018
First available in Project Euclid: 30 December 2016

zbMATH: 06873721
MathSciNet: MR3737946
Digital Object Identifier: 10.1214/16-BA1045

JOURNAL ARTICLE
24 PAGES


SHARE
Vol.13 • No. 1 • March 2018
Back to Top