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Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint
Authors
A. Burnetas Kanavetas, O.
Publication date
1 January 2012
Publisher
Abstract
We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulation. © 2012, Springer Science+Business Media New York
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Last time updated on 10/02/2023