An important step in designing stochastic simulation is modeling the uncertainty in the input environment of the system being studied. Obtaining a reasonable representation of this uncertainty can be challenging in the presence of dependencies in the input process. This tutorial attempts to provide a coherent narrative of the central principles that underlie methods that aim to model and sample a wide variety of dependent input processes.
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Biller et al. (2005) studied this question.
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