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

ISBN-13: 9780367658465 / Angielski / Miękka / 2020 / 336 str.

Emmanuel Gobet

Opis

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-Car

Spis treści

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.

O autorze

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.