Abstract
It is often the case that one would like to simulate a few paths of a particular model in order to gain insight into its possible behavior. Sometimes one would like to go further and simulate many paths in order to perform Monte Carlo experiments and produce estimates of expectations. In this chapter, we provide an overview of numerical methods for stochastically modeled biochemical systems. We briefly introduce the basic ideas behind Monte Carlo estimation and discuss ways to generate the necessary random variables via transformations of uniform random variables. We introduce two methods that provide statistically exact sample paths, and one method that provides approximate sample paths. Finally, we introduce the multi-level Monte Carlo estimator for the efficient computation of expectations.
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Anderson, D.F., Kurtz, T.G. (2015). Numerical methods. In: Stochastic Analysis of Biochemical Systems. Mathematical Biosciences Institute Lecture Series(), vol 1.2. Springer, Cham. https://doi.org/10.1007/978-3-319-16895-1_5
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DOI: https://doi.org/10.1007/978-3-319-16895-1_5
Publisher Name: Springer, Cham
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