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Modeling with Itˆ
o Stochastic Differential Equations

Theory and Applications
This series is aimed at publishing work dealing with the definition, development
and application of fundamental theory and methodology, computational and
algorithmic implementations and comprehensive empirical studies in mathematical modelling. Work on new mathematics inspired by the construction of
mathematical models, combining theory and experiment and furthering the
understanding of the systems being modelled are particularly welcomed.
Manuscripts to be considered for publication lie within the following, nonexhaustive list of areas: mathematical modelling in engineering, industrial
mathematics, control theory, operations research, decision theory, economic
modelling, mathematical programming, mathematical system theory, geophysical sciences, climate modelling, environmental processes, mathematical modelling in psychology, political science, sociology and behavioural sciences,
mathematical biology, mathematical ecology, image processing, computer
vision, artificial intelligence, fuzzy systems, and approximate reasoning, genetic
algorithms, neural networks, expert systems, pattern recognition, clustering,
chaos and fractals.
Original monographs, comprehensive surveys as well as edited collections will
be considered for publication.
Managing Editor:
R. Lowen (Antwerp, Belgium)

Series Editors:
A. Stevens (Max Planck Institute for Mathematics in the Sciences, Leipzig,
R. Laubenbacher, (Virginia Bioinformatics Institute, Virginia Tech, USA)

The titles published in this series are listed at the end of this volume.

Modeling with Itˆ
Stochastic Differential

E. Allen
Texas Tech University, USA

A C.I.P. Catalogue record for this book is available from the Library of Congress.

ISBN-13 978-1-4020-5952-0 (HB)
ISBN-13 978-1-4020-5953-7 (e-book)

Published by Springer,
P.O. Box 17, 3300 AA Dordrecht, The Netherlands.

Printed on acid-free paper

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c 2007 Springer
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To Linda and Anna

Modeling with Itˆo Stochastic Differential Equations
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