Stochastic Simulation and Monte Carlo Methods Mathematical Foundations of Stochastic Simulation /

• Main Authors: Graham, Carl. (Author), Talay, Denis. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Series: Stochastic Modelling and Applied Probability, 68
• Subjects: Mathematics.; Finance.; Numerical analysis.; Distribution (Probability theory).; Probability Theory and Stochastic Processes.; Numerical Analysis.; Quantitative Finance.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-39363-1

In various scientific and industrial fields, stochastic simulations are taking on a new importance. This is due to the increasing power of computers and practitioners<U+0019> aim to simulate more and more complex systems, and thus use random parameters as well as random noises to model the par...

Table of Contents:

• Part I:Principles of Monte Carlo Methods
• 1.Introduction
• 2.Strong Law of Large Numbers and Monte Carlo Methods
• 3.Non Asymptotic Error Estimates for Monte Carlo Methods
• Part II:Exact and Approximate Simulation of Markov Processes
• 4.Poisson Processes
• 5.Discrete-Space Markov Processes
• 6.Continuous-Space Markov Processes with Jumps
• 7.Discretization of Stochastic Differential Equations
• Part III:Variance Reduction, Girsanov<U+0019>s Theorem, and Stochastic Algorithms
• 8.Variance Reduction and Stochastic Differential Equations
• 9.Stochastic Algorithms
• References
• Index.