Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint.
Fri, 01/20/2012 - 15:01 — SomNambul
link: http://arxiv.org/abs/1201.4002
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.
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