Emmanuel Gobet

Monte-Carlo Methods and Stochastic Processes

From Linear to Non-Linear

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 336
• Erscheinungstermin: 1. August 2016
• Englisch
• Abmessung: 234mm x 160mm x 23mm
• Gewicht: 612g
• ISBN-13: 9781498746229
• ISBN-10: 1498746225
• Artikelnr.: 44494494

Autorenporträt

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.

Inhaltsangabe

Introduction: brief overview of Monte-Carlo methods. TOOLBOX FOR STOCHASTIC
SIMULATION: Generating random variables. Convergences and error estimates.
Variance reduction. SIMULATION OF LINEAR PROCESS: Stochastic differential
equations and Feynman-Kac formulas. Euler scheme for stochastic
differential equations. Statistical error in the simulation of stochastic
differential equations. SIMULATION OF NONLINEAR PROCESS: Backward
stochastic differential equations. Simulation by empirical regression.
Interacting particles and non-linear equations in the McKean sense.
Appendix. Index.