Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
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)
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Recent advances in ranking and selection
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
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
Stochastic kriging for simulation metamodeling
Proceedings of the 40th Conference on Winter Simulation
Economic Analysis of Simulation Selection Problems
Management Science
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
Stochastic Kriging for Simulation Metamodeling
Operations Research
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Sequential Sampling with Economics of Selection Procedures
Management Science
Value of information methods for pairwise sampling with correlations
Proceedings of the Winter Simulation Conference
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Bayesian statistics comprises a powerful set of methods for analyzing simulated systems. Combined with dynamic programming and other methods for sequential decision making under uncertainty, Bayesian methods have been used to design algorithms for finding the best of several simulated systems. When the dynamic program can be solved exactly, these algorithms have optimal average-case performance. In other situations, this dynamic programming analysis supports the development of approximate methods with sub-optimal but nevertheless good average-case performance. These methods with good average-case performance are particularly useful when the cost of simulation prevents the use of procedures with worst-case statistical performance guarantees. We provide an overview of Bayesian methods used for selecting the best, providing an in-depth treatment of the simpler case of ranking and selection with independent priors appropriate for smaller-scale problems, and then discussing how these same ideas can be applied to correlated priors appropriate for large-scale problems.