Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear

This text focuses on the simulation of stochastic processes in continuous time and their link with PDEs. It covers linear and nonlinear problems in biology, finance, geophysics, mechanics, chemistry, and other application areas. The text also thoroughly develops the problem of numerical integration and computation of expectation by the Monte-Carlo method. It presents basic tools for stochastic simulation and analysis of algorithm convergence, describes Monte-Carlo methods for the simulation of stochastic differential equations, and discusses the simulation of non-linear dynamics.

Author: Emmanuel Gobet
Publisher: CRC Press
Published: 08/01/2016
Pages: 336
Binding Type: Hardcover
Weight: 1.35lbs
Size: 9.20h x 6.30w x 0.90d
ISBN: 9781498746229

About the Author

Emmanuel Gobet is a professor of applied mathematics at Ecole Polytechnique. His research interests include algorithms of probabilistic type and stochastic approximations, financial mathematics, Malliavin calculus and stochastic analysis, Monte Carlo simulations, statistics for stochastic processes, and statistical learning.