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arXiv · 2111.14534

Efficient Sampling Policy for Selecting a Good Enough Subset

Gongbo Zhang·Bin Chen·Qing-shan Jia·Yijie Peng
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Abstract

The note studies the problem of selecting a good enough subset out of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior probability of correctly selecting a good subset. We formulate the dynamic sampling decision as a stochastic control problem in a Bayesian setting. In an approximate dynamic programming paradigm, we propose a sequential sampling policy based on value function approximation. We analyze the asymptotic property of the proposed sampling policy. Numerical experiments demonstrate the efficiency of the proposed procedure.

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Gongbo Zhang, Bin Chen, Qing-shan Jia, Yijie Peng. 2021-11-29. Efficient Sampling Policy for Selecting a Good Enough Subset. https://doi.org/10.1109/tac.2022.3207871

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