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Context-dependent Ranking and Selection under a Bayesian Framework

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arxiv 2012.05577 v2 pith:IXZTFHMZ submitted 2020-12-10 stat.ME

Haidong Li , Henry Lam , Zhe Liang , Yijie Peng This is my paper · ORCID
classification stat.ME
keywords context-dependentrankingsamplingschemeselectionbayesianbestcontexts
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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.

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