Journal
INFORMS JOURNAL ON COMPUTING
Volume 21, Issue 4, Pages 599-613Publisher
INFORMS
DOI: 10.1287/ijoc.1080.0314
Keywords
simulation: design of experiments; decision analysis: sequential; statistics; Bayesian
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We consider a Bayesian ranking and selection problem with independent normal rewards and a correlated multivariate normal belief on the mean values of these rewards. Because this formulation of the ranking and selection problem models dependence between alternatives' mean values, algorithms may use this dependence to perform efficiently even when the number of alternatives is very large. We propose a fully sequential sampling policy called the knowledge-gradient policy, which is provably optimal in some special cases and has bounded suboptimality in all others. We then demonstrate how this policy may be applied to efficiently maximize a continuous function on a continuous domain while constrained to a fixed number of noisy measurements.
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