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Stochastic Analysis and Diffusion Processes

Download or Read eBook Stochastic Analysis and Diffusion Processes PDF written by Gopinath Kallianpur and published by OUP Oxford. This book was released on 2014-01-09 with total page 368 pages. Available in PDF, EPUB and Kindle.
Stochastic Analysis and Diffusion Processes
Author :
Publisher : OUP Oxford
Total Pages : 368
Release :
ISBN-10 : 9780191004520
ISBN-13 : 0191004529
Rating : 4/5 (20 Downloads)

Book Synopsis Stochastic Analysis and Diffusion Processes by : Gopinath Kallianpur

Book excerpt: Stochastic Analysis and Diffusion Processes presents a simple, mathematical introduction to Stochastic Calculus and its applications. The book builds the basic theory and offers a careful account of important research directions in Stochastic Analysis. The breadth and power of Stochastic Analysis, and probabilistic behavior of diffusion processes are told without compromising on the mathematical details. Starting with the construction of stochastic processes, the book introduces Brownian motion and martingales. The book proceeds to construct stochastic integrals, establish the Itô formula, and discuss its applications. Next, attention is focused on stochastic differential equations (SDEs) which arise in modeling physical phenomena, perturbed by random forces. Diffusion processes are solutions of SDEs and form the main theme of this book. The Stroock-Varadhan martingale problem, the connection between diffusion processes and partial differential equations, Gaussian solutions of SDEs, and Markov processes with jumps are presented in successive chapters. The book culminates with a careful treatment of important research topics such as invariant measures, ergodic behavior, and large deviation principle for diffusions. Examples are given throughout the book to illustrate concepts and results. In addition, exercises are given at the end of each chapter that will help the reader to understand the concepts better. The book is written for graduate students, young researchers and applied scientists who are interested in stochastic processes and their applications. The reader is assumed to be familiar with probability theory at graduate level. The book can be used as a text for a graduate course on Stochastic Analysis.


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