Numerical Methods for Stochastic Processes by Nicolas BouleauNumerical Methods for Stochastic Processes by Nicolas Bouleau

Numerical Methods for Stochastic Processes

byNicolas Bouleau, Dominique Lépingle

Hardcover | December 31, 1993

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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.
Title:Numerical Methods for Stochastic ProcessesFormat:HardcoverDimensions:384 pages, 9.53 × 6.32 × 1.07 inPublished:December 31, 1993Publisher:Wiley

The following ISBNs are associated with this title:

ISBN - 10:0471546410

ISBN - 13:9780471546412

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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.

From Our Editors

In recent years, random variables and stochastic processes have emerged as important factors in predicting outcomes in virtually every field of applied and social science. Ironically, according to Nicolas Bouleau and Dominique Lepingle, the presence of randomness in the model sometimes leads engineers to accept crude mathematical treatments that produce inaccurate results. The purpose of Numerical Methods for Stochastic Processes is to add greater rigor to numerical treatment of stochastic processes so that they produce results that can be relied upon when making decisions and assessing risks. Based on a postgraduate course given by the authors at Paris 6 University, the text emphasizes simulation methods, which can now be implemented with specialized computer programs. Specifically presented are the Monte Carlo and shift methods, which use an "imitation of randomness" and have a wide range of applications, and the so-called quasi-Monte Carlo methods, which are rigorous but less widely applicable. Offering a broad introduction to the field, this book presents the