论文标题
贝叶斯框架下的与上下文相关的排名和选择
Context-dependent Ranking and Selection under a Bayesian Framework
论文作者
论文摘要
我们考虑一个与上下文有关的排名和选择问题。最好的设计不是通用，而是取决于上下文。在贝叶斯框架下，我们为上下文依赖性优化（DSCO）开发了动态抽样方案，以有效地学习和选择所有上下文中的最佳设计。事实证明，提出的抽样方案是一致的。数值实验表明，所提出的抽样方案显着提高了上下文依赖性排名和选择的效率。
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.
