Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint

Apostolos Burnetas () and Odysseas Kanavetas ()
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Apostolos Burnetas: University of Athens
Odysseas Kanavetas: University of Athens

Chapter Chapter 8 in Applications of Mathematics and Informatics in Military Science, 2012, pp 97-112 from Springer

Abstract: 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.

Keywords: Stochastic learning and adaptive control; Sequential design; Sampling cost constraint (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4614-4109-0_8

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DOI: 10.1007/978-1-4614-4109-0_8

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