Title | ||
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Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint |
Abstract | ||
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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. |
Year | DOI | Venue |
---|---|---|
2012 | 10.1007/978-1-4614-4109-0_8 | arXiv: Machine Learning |
Field | DocType | Volume |
Sequential sampling,Mathematical optimization,Cost constraint,Finite set,Upper and lower bounds,Rate of convergence,Sampling (statistics),Sequential analysis,Mathematics,Complete information | Journal | abs/1201.4002 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Apostolos Burnetas | 1 | 133 | 14.27 |
Odysseas Kanavetas | 2 | 0 | 1.01 |