Theory and Applications of Stochastic Processes: An Analytical Approach / Edition 1

Theory and Applications of Stochastic Processes: An Analytical Approach / Edition 1

by Zeev Schuss
     
 

ISBN-10: 1441916040

ISBN-13: 9781441916044

Pub. Date: 12/21/2009

Publisher: Springer New York

Stochastic processes and diffusion theory are the mathematical underpinnings of many scientific disciplines, including statistical physics, physical chemistry, molecular biophysics, communications theory and many more. Many books, reviews and research articles have been published on this topic, from the purely mathematical to the most practical.

This book offers

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Overview

Stochastic processes and diffusion theory are the mathematical underpinnings of many scientific disciplines, including statistical physics, physical chemistry, molecular biophysics, communications theory and many more. Many books, reviews and research articles have been published on this topic, from the purely mathematical to the most practical.

This book offers an analytical approach to stochastic processes that are most common in the physical and life sciences, as well as in optimal control and in the theory of filltering of signals from noisy measurements. Its aim is to make probability theory in function space readily accessible to scientists trained in the traditional methods of applied mathematics, such as integral, ordinary, and partial differential equations and asymptotic methods, rather than in probability and measure theory.

Product Details

ISBN-13:
9781441916044
Publisher:
Springer New York
Publication date:
12/21/2009
Series:
Applied Mathematical Sciences Series, #170
Edition description:
2010
Pages:
468
Product dimensions:
6.40(w) x 9.30(h) x 1.40(d)

Table of Contents

The Physical Brownian Motion: Diffusion And Noise.- The Probability Space of Brownian Motion.- Itô Integration and Calculus.- Stochastic Differential Equations.- The Discrete Approach and Boundary Behavior.- The First Passage Time of Diffusions.- Markov Processes and their Diffusion Approximations.- Diffusion Approximations to Langevin’s Equation.- Large Deviations of Markovian Jump Processes.- Noise-Induced Escape From an Attractor.- Stochastic Stability.

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