The book shows how to formulate problems in probability theory in terms of differential, difference, integral, and partial differential equations and how to solve them by the approximation methods of applied mathematics.

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.

A wealth of examples from physics, chemistry, biology and engineering Treads a route between probabilists and physical scientists Includes many physically relevant results and discussions Includes supplementary material: sn.pub/extras

Klappentext

This book offers an analytical approach to stochastic processes that are most common in the physical and life sciences. Its aim is to make probability theory readily accessible to scientists trained in the traditional methods of applied mathematics, such as integral, ordinary, and partial differential equations and in asymptotic methods, rather than in probability and measure theory. It shows how to derive explicit expressions for quantities of interest by solving equations. Emphasis is put on rational modeling and approximation methods.

The book includes many detailed illustrations, applications, examples and exercises. It will appeal to graduate students and researchers in mathematics, physics and engineering.

Inhalt
The Physical Brownian Motion: Diffusion And Noise.- The Probability Space of Brownian Motion.- It#x00F4; 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#x2019;s Equation.- Large Deviations of Markovian Jump Processes.- Noise-Induced Escape From an Attractor.- Stochastic Stability.