Abstract
Each function value in a stochastic program can involve a multidimensional integral in extremely high dimensions. Because Monte Carlo simulation appears to offer the best possibilities for higher dimensions (see, e.g., Deák [1988] and Asmussen and Glynn [2007]), it seems to be the natural choice for use in stochastic programs. In this chapter, we describe some of the basic approaches built on sampling methods. The key feature is the use of statistical estimates to obtain confidence intervals on results. Some of the material uses probability measure theory which is necessary to develop the analytical results.
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© 2011 Springer Science+Business Media, LLC
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Birge, J.R., Louveaux, F. (2011). Monte Carlo Methods. In: Introduction to Stochastic Programming. Springer Series in Operations Research and Financial Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0237-4_9
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DOI: https://doi.org/10.1007/978-1-4614-0237-4_9
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Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-0236-7
Online ISBN: 978-1-4614-0237-4
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