Applied Stochastic Differential Equations

Stochastic differential equations are differential equations with stochastic solutions, useful for modeling uncertainties and noisy phenomena. This book covers their applications in target tracking and medical technology, with an emphasis on solution methods and practical examples. MATLAB/Octave source code is available for download.

Format: Paperback / softback
Length: 326 pages
Publication date: 02 May 2019
Publisher: Cambridge University Press

Stochastic differential equations (SDEs) are a class of differential equations that yield solutions in the form of stochastic processes. These equations possess fascinating mathematical characteristics that make them highly valuable for modeling uncertainties and noisy phenomena across a wide range of disciplines. This book is driven by the practical applications of SDEs in target tracking and medical technology, particularly in methodologies such as filtering, smoothing, parameter estimation, and machine learning. Its primary objective is to provide a comprehensive and intuitive understanding of stochastic differential equations, while also covering the fundamental concepts of Itô calculus, central theorems in the field, and approximation schemes like stochastic Runge–Kutta.

Greater emphasis is placed on solution methods than on the analysis of theoretical properties of the equations. The book adopts a practical approach, assuming prior knowledge of ordinary differential equations. It includes numerous worked examples and end-of-chapter exercises that feature application-driven derivations and computational assignments, encouraging hands-on work with the methods. Additionally, MATLAB/Octave source code is available for download, facilitating practical exploration and experimentation with the techniques presented in the book.

Weight: 488g
Dimension: 152 x 229 x 22 (mm)
ISBN-13: 9781316649466