Verwandte Artikel zu Theory and Applications of Stochastic Processes: An...
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Inhaltsangabe
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.
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Review
From the reviews:
“It intends to investigate the role of stochastic processes in questions from physics or engineering, to explore the strong relationship between stochastic and partial differential equations and to provide analytical approximations to the solutions. ... The book will be very helpful to people working in disciplines like statistical physics, physical chemistry, molecular biophysics, and communications theory. ... Endowed with numerous exercises and worked-out examples, it may also provide graduate students with a well-thought-out two-semester course on stochastic processes and their applications.” (Dominique Lépingle, Mathematical Reviews, Issue 2011 d)
“This book is something for which many have been long awaiting. It provides a rather in-depth presentation of the analytical approach to stochastic processes with continuous paths. ... The methods developed in the text are very accessible to applied mathematicians with a basic background in probability theory. This is a timely and unique contribution, from one of its leading researchers over four decades, to an exciting area of applied mathematics with ever-growing importance.” (Hong Qian, SIAM Review, Vol. 53 (1), 2011)
From the Back Cover
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.
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