<p>This book explains a procedure for constructing realistic stochastic differential equation models for randomly varying systems in biology, chemistry, physics, engineering, and finance. Introductory chapters present the fundamental concepts of random variables, stochastic processes, stochastic integration, and stochastic differential equations. These concepts are explained in a Hilbert space setting which unifies and simplifies the presentation.</p>

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From the reviews:

"The author of this book has carefully selected and well described basic notions and concepts from probability theory and stochastic processes ... . His goal is ... to address the book to a wide category of readers, applied scientists, who need to use these sophisticated tools in their work. ... Besides researchers ... this book is suitable as a text for graduate university courses. I enjoyed reading the book and my expectation is that it will be met with interest by the readers." (Jordan M. Stoyanov, Zentralblatt MATH, Vol. 1130, 2008)

"This text sets out to provide a reasonably concise and accessible account of the extensive range of concepts and procedures that are used in producing and handling SDEMs, and by and large it succeeds. ... On the whole, the selection of material is very good; the author has succeeded in producing an account of the subject that is manageably compact and yet reasonably wide-ranging in its illustrative applications. ... the book can indeed be firmly recommended." (David Stirzaker, SIAM Review, Vol. 50 (2), 2008)

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