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

Gobet, Emmanuel

Softcover

ISBN 10: 0367658461 ISBN 13: 9780367658465

Publisher: Chapman and Hall/CRC, 2020

Synopsis

Developed from the author’s course at the Ecole Polytechnique, Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (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.

The book begins with a history of Monte-Carlo methods and an overview of three typical Monte-Carlo problems: numerical integration and computation of expectation, simulation of complex distributions, and stochastic optimization. The remainder of the text is organized in three parts of progressive difficulty. The first part presents basic tools for stochastic simulation and analysis of algorithm convergence. The second part describes Monte-Carlo methods for the simulation of stochastic differential equations. The final part discusses the simulation of non-linear dynamics.

About this title

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.

Edition details

• Publisher: Chapman and Hall/CRC
• Publication date: 2020
• Language: English
• ISBN 10: 0367658461
• ISBN 13: 9780367658465
• Binding: Paperback
• Edition number: 1
• Number of pages: 336