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    Title: A note on strategies for bandit problems with infinitely many arms
    Authors: 陳功宇;Chen, Kung-yu;林千代;Lin, Chien-tai
    Contributors: 淡江大學數學學系
    Keywords: K-failure strategy;M-run strategy;Nn-learning strategy;Non-recalling m-run strategy
    k-failure strategy;m-run strategy;Nn-learning strategy;non-recalling m-run strategy
    Date: 2004-05
    Issue Date: 2010-01-28 07:32:01 (UTC+8)
    Publisher: Springer
    Abstract: A bandit problem consisting of a sequence of n choices (n→∞) from a number of infinitely many Bernoulli arms is considered. The parameters of Bernoulli arms are independent and identically distributed random variables from a common distribution F on the interval [0,1] and F is continuous with F(0)=0 and F(1)=1. The goal is to investigate the asymptotic expected failure rates of k-failure strategies, and obtain a lower bound for the expected failure proportion over all strategies presented in Berry et al. (1997). We show that the asymptotic expected failure rates of k-failure strategies when 0<b≤1 and a lower bound can be evaluated if the limit of the ratio F(1)−F(t) versus (1−t)b exists as t→1− for some b>0.
    Relation: Metrika 59(2), pp.193-203
    DOI: 10.1007/s001840300279
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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