Abstract
In the previous chapter, we were concerned almost exclusively with the problem of how to generate independent random variables with a specified distribution. In the simplest settings, this is the underlying statistical model and we need go no further. In many other situations, we have to simulate some sort of dependent data or noise process to act as inputs to our simulation model. Many dependent stochastic models can be simulated in an obvious way from their definitions. Nevertheless, some tricks sometimes are useful and we present a few of the more common ones below.
All models are wrong, some models are useful.
George Box
Everything should be made as simple as possible, but not simpler.
Albert Einstein
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© 2004 Springer Science+Business Media New York
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Bucklew, J.A. (2004). Stochastic Models. In: Introduction to Rare Event Simulation. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-4078-3_2
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DOI: https://doi.org/10.1007/978-1-4757-4078-3_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-1893-2
Online ISBN: 978-1-4757-4078-3
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