Context-dependent Ranking and Selection under a Bayesian Framework
Methodology
2020-12-21 v2
Abstract
We consider a context-dependent ranking and selection problem. The best design is not universal but depends on the contexts. Under a Bayesian framework, we develop a dynamic sampling scheme for context-dependent optimization (DSCO) to efficiently learn and select the best designs in all contexts. The proposed sampling scheme is proved to be consistent. Numerical experiments show that the proposed sampling scheme significantly improves the efficiency in context-dependent ranking and selection.
Cite
@article{arxiv.2012.05577,
title = {Context-dependent Ranking and Selection under a Bayesian Framework},
author = {Haidong Li and Henry Lam and Zhe Liang and Yijie Peng},
journal= {arXiv preprint arXiv:2012.05577},
year = {2020}
}
Comments
The article was published without the co-Author's notice, and it is withdrawn due to his objection