Numerical Methods for Stochastic Processes

by Nicolas Bouleau, Dominique Lépingle

Overview

Gives greater rigor to numerical treatments of stochastic models. Contains Monte Carlo and quasi-Monte Carlo techniques, simulation of major stochastic procedures, deterministic methods adapted to Markovian problems and special problems related to stochastic integral and differential equations. Simulation methods are given throughout the text as well as numerous exercises.

Product Details

ISBN-13:	9780471546412
Publisher:	Wiley
Publication date:	01/14/1994
Series:	Wiley Series in Probability and Statistics , #273
Pages:	384
Product dimensions:	6.32(w) x 9.53(h) x 1.07(d)

About the Author

Nicolas Bouleau is a mathematician, philosopher of science and essayist, professor at Ecole des Ponts Paris Tech. He was responsible for introducing computer simulation into the teaching of probability and was among the first to develop research in mathematical finance in France. Dominique Lépingle is the author of Numerical Methods for Stochastic Processes, published by Wiley.

Table of Contents

Preliminaries.

Computation of Expectations in Finite Dimension.

Simulation of Random Processes.

Deterministic Resolution of Some Markovian Problems.

Stochastic Differential Equations and Brownian Functionals.

Notes.

References.

Index.

Editorial Reviews

The authors offer this text for an introductory graduate course in an effort to add greater rigor to numerical treatment of stochastic processes, emphasizing simulation methods implemented with computer programs. They offer a broad introduction and theoretical background to the field, present the Monte Carlo, quasi-Monte Carlo, and shift methods, and explore problems raised by these methods. Features exercises and b&w graphs. Of interest to professional statisticians, engineers, and physical and social scientists, as well as students. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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