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.
CITATION STYLE
Burnetas, A., & Kanavetas, O. (2012). Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint. In Springer Optimization and Its Applications (Vol. 71, pp. 97–112). Springer. https://doi.org/10.1007/978-1-4614-4109-0_8
Mendeley helps you to discover research relevant for your work.