Matches in SemOpenAlex for { <https://semopenalex.org/work/W2111066976> ?p ?o ?g. }
- W2111066976 abstract "We consider the class of fully sequential Bayesian information collection problems, a class that includes ranking and selection problems, multi-armed bandit problems, and many others. Although optimal policies for such problems are generally known to exist and to satisfy Bellman’s recursion, the curses of dimensionality prevent us from actually computing them except in a few very special cases. Motivated by this difficulty, we develop a general class of practical and theoretically well-founded information collection policies known as knowledge-gradient (KG) policies. KG policies have several attractive qualities: they are myopically optimal in general; they are asymptotically optimal in a broad class of problems; they are flexible and may be computed easily in a broad class of problems; and they perform well numerically in several well-studied ranking and selection problems compared with other state-of-the-art policies designed specifically for these problems." @default.
- W2111066976 created "2016-06-24" @default.
- W2111066976 creator A5039019367 @default.
- W2111066976 date "2009-01-01" @default.
- W2111066976 modified "2023-09-27" @default.
- W2111066976 title "Knowledge-Gradient Methods for Statistical Learning" @default.
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