A Bonferroni selection procedure when using commom random numbers with unknown variances
WSC '86 Proceedings of the 18th conference on Winter simulation
Comparison with a standard via fully sequential procedures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling
INFORMS Journal on Computing
A Knowledge-Gradient Policy for Sequential Information Collection
SIAM Journal on Control and Optimization
The knowledge-gradient stopping rule for ranking and selection
Proceedings of the 40th Conference on Winter Simulation
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
INFORMS Journal on Computing
Optimization via simulation with Bayesian statistics and dynamic programming
Proceedings of the Winter Simulation Conference
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We consider optimization via simulation over a finite set of alternatives. We employ a Bayesian value-of-information approach in which we allow both correlated prior beliefs on the sampling means and correlated sampling. Correlation in the prior belief allow us to learn about an alternative's value from samples of similar alternatives. Correlation in sampling, achieved through common random numbers, allows us to reduce the variance in comparing one alternative to another. We allow for a more general combination of both types of correlation than has been offered previously in the Bayesian ranking and selection literature. We do so by giving an exact expression for the value of information for sampling the difference between a pair of alternatives, and derive new knowledge-gradient methods based on this valuation.