Bayesian methods are useful in the simulation context for several reasons. They provide a convenient and useful way to represent uncertainty about alternatives (like manufacturing system designs, service operations, or other simulation applications) in a way that quantifies uncertainty about the performance of systems, or about inputs parameters of those systems. They also can be used to improve the efficiency of discrete optimization with simulation and response surface methods. Bayesian methods work well with other decision theoretic tools, and can therefore provide a link from traditional operations-level experiments to higher-level managerial decision-making needs, in addition to improving the efficiency of computer experiments.
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