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- W3149180552 abstract "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." @default.
- W3149180552 created "2021-04-13" @default.
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- W3149180552 date "2020-12-14" @default.
- W3149180552 modified "2023-10-14" @default.
- W3149180552 title "Context-Dependent Ranking and Selection under a Bayesian Framework" @default.
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- W3149180552 doi "https://doi.org/10.1109/wsc48552.2020.9383965" @default.
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