Title
Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint
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.
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 Burnetas113314.27
Odysseas Kanavetas201.01