Titlebook: Bandit problems; Sequential Allocatio Donald A. Berry,Bert Fristedt Book 1985 D. A. Berry and B. Fristedt 1985 Calculation.Counting.Mathema

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Springers Handbücher der Rechtswissenschafts has been our convention in the Bernoulli case, we regard .. as a distribution on the Bernoulli parameter .. ∈ [0, 1] rather than on .. ∈ D; and consistent with an earlier modification of notation, we write the conditional distribution of (.., ..,...,..) given success on arm 1, say, as. and given a

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https://doi.org/10.1007/978-3-7091-8265-9died in .., now abbreviated to ., the distribution of the random measure ... For arbitrary . we can, without loss, assume that arm 2 always produces the known observation . Since . is given by the pair (.), we now speak of the (., .; .)-bandit.

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https://doi.org/10.1007/978-3-7091-8265-9en the problem is to maximize the sum of . observations. When . is unknown the corresponding random discount sequence can be taken to be nonrandom (see Section 3.1); it can be any nonincreasing sequence depending on the uncertainty in .. As a special case suppose . has a geometric distribution; so t

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Handbuch diagnostische Radiologience . has horizon n and is uniform: . . = ... = . . = 1 and . . = . . = ... = 0. Such uniform discounting has been considered extensively through examples in the first five chapters of this book, and in the literature generally. The objective implicit in uniform discounting is to maximize the expect

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