Optimizing Input Data Acquisition for Ranking and Selection: A View Through the Most Probable Best
Optimizing Input Data Acquisition for Ranking and Selection: A View Through the Most Probable Best
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DOI：
10.1109/wsc57314.2022.10015453
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Taeho Kim;Eunhye Song
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文献类型：
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作者：
Taeho Kim;Eunhye Song
本文研究了输入不确定条件下的贝叶斯排序与选择问题。我们假设有多个独立的输入数据源，从中可以收集额外的数据，以减少输入的不确定性。为了优化输入数据采集，我们首先证明了最可能最佳(MPB) -具有最大后验最优概率（后验偏好）的解决方案-是现实世界最优的强一致估计量。我们从输入数据源中研究最优的渐近静态抽样比率，使MPB的后验偏好的指数收敛率最大化。然后，我们创建一个顺序采样规则来平衡模拟和输入数据收集工作。该算法以解质量的后验置信度停止。
This paper concerns a Bayesian ranking and selection (R&S) problem under input uncertainty when all solutions are simulated with common input models estimated from data. We assume that there are multiple independent input data sources from which additional data can be collected at a cost to reduce input uncertainty. To optimize input data acquisition, we first show that the most probable best (MPB)―the solution with the largest posterior probability of being optimal (posterior preference)―is a strongly consistent estimator for the real-world optimum. We investigate the optimal asymptotic static sampling ratios from the input data sources that maximizes the exponential convergence rate of the MPB's posterior preference. We then create a sequential sampling rule that balances the simulation and input data collection effort. The proposed algorithm stops with posterior confidence in the solution quality.