Sequential Sampling for Bayesian Robust Ranking and Selection
Dec 10, 2016·
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0 min read
Xiaowei Zhang
Liang Ding
Abstract
We consider a Bayesian ranking and selection problem in the presence of input distribution uncertainty. The distribution uncertainty is treated from a robust perspective. A naive extension of the knowledge gradient (KG) policy fails to converge in the new robust setting. We propose several stationary policies that extend KG in various aspects. Numerical experiments show that the proposed policies have excellent performance in terms of both probability of correction selection and normalized opportunity cost.
Type
Publication
Proceedings of the 2016 Winter Simulation Conference, 758–769
Authors
I am an Associate Professor at HKUST, jointly appointed in the Department of Industrial Engineering and Decision Analytics and the Department of Economics, and the Academic Director of the MSc in FinTech program. I serve as an Associate Editor for several leading journals in the field, including Management Science, Operations Research, Navel Research Logistics, and Queueing Systems.
