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首页> 外文期刊>ACM Transactions on Modeling and Computer Simulation >Replicated Computations Results (RCR) Report for 'Reusing Search Data in Ranking and Selection: What Could Possibly Go Wrong?'
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Replicated Computations Results (RCR) Report for 'Reusing Search Data in Ranking and Selection: What Could Possibly Go Wrong?'

机译：“在排名和选择中重用搜索数据：可能出错的原因”的复制计算结果（RCR）报告。

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摘要

"Reusing Search Data in Ranking and Selection: What Could Possibly Go Wrong?" [2] by Eckman and Henderson rigorously defines the statistical guarantees for ranking-and-selection (R&S) procedures after random search, and points out that the simulation replications collected in the search phase are conditionally dependent given the sequence of returned systems. Therefore, reusing the search data for R&S may affect the statistical guarantees. The authors further design random search algorithms to demonstrate that the correct selection guarantees of some ranking-and-selection procedures will be compromised if reusing the simulation replications taken during the search. This replicated computation report focuses on the reproducibility of the experiment results in the aforementioned article.
机译：Eckman和Henderson撰写的“在排名和选择中重用搜索数据：可能会出错吗？” [2]严格定义了随机搜索后排名和选择（R＆S）过程的统计保证，并指出了模拟给定返回系统的顺序，在搜索阶段收集的复制将有条件地依赖。因此，将搜索数据重新用于R＆S可能会影响统计保证。作者进一步设计了随机搜索算法，以证明如果重新使用在搜索过程中进行的模拟复制，则会损害某些排名和选择过程的正确选择保证。该复制的计算报告着重于上述文章中实验结果的可重复性。

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