在排名和选择中达到“最优价格”
Getting to "rate-optimal'' in ranking & selection
摘要：
在他们2004年的开创性论文中，Glynn和Juneja正式而精确地建立了速率最优，错误选择概率，复制分配方案，用于选择k个模拟系统中的最佳系统。在独立的正态分布输出的情况下，这种分配有一种简单的形式，它以直观吸引人的方式依赖于真实的均值和方差。当然，均值和（通常）方差是未知的，但速率最优分配为可实现的、动态的、数据驱动的策略提供了一个目标。在本文中，我们比较了四个相关的复制分配政策的经验行为：mCEI从陈和Rzyhov和我们的新gCEI政策，都收敛到Glynn和Juneja分配; AOMAP从彭和傅收敛到OCBA最优分配;和TTTS从Russo的目标的收敛速度的后验概率的不正确的选择。我们发现，这些政策有明显不同的行为在某些设置。
英文摘要：
In their 2004 seminal paper, Glynn and Juneja formally and precisely established the rate-optimal, probability-of-incorrect-selection, replication allocation scheme for selecting the best of k simulated systems. In the case of independent, normally distributed outputs this allocation has a simple form that depends in an intuitively appealing way on the true means and variances. Of course the means and (typically) variances are unknown, but the rate-optimal allocation provides a target for implementable, dynamic, data-driven policies to achieve. In this paper we compare the empirical behavior of four related replication-allocation policies: mCEI from Chen and Rzyhov and our new gCEI policy that both converge to the Glynn and Juneja allocation; AOMAP from Peng and Fu that converges to the OCBA optimal allocation; and TTTS from Russo that targets the rate of convergence of the posterior probability of incorrect selection. We find that these policies have distinctly different behavior in some settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
