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Applied Stochastic Processes

Ming Liao



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CRC Press Inc
22 July 2013
Applied Stochastic Processes presents a concise, graduate-level treatment of the subject, emphasizing applications and practical computation. It also establishes the complete mathematical theory in an accessible way.

After reviewing basic probability, the text covers Poisson processes, renewal processes, discrete- and continuous-time Markov chains, and Brownian motion. It also offers an introduction to stochastic differential equations. While the main applications described are queues, the book also considers other examples, such as the mathematical model of a single stock market.

With exercises in most sections, this book provides a clear, practical introduction for beginning graduate students. The material is presented in a straightforward manner using short, motivating examples. In addition, the author develops the mathematical theory with a strong emphasis on probability intuition.
By:   Ming Liao
Imprint:   CRC Press Inc
Country of Publication:   United States
Dimensions:   Height: 234mm,  Width: 156mm,  Spine: 20mm
Weight:   499g
ISBN:   9781466589339
ISBN 10:   1466589337
Pages:   208
Publication Date:   22 July 2013
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

Ming Liao is a professor in the Department of Mathematics and Statistics at Auburn University. He has published 45 research papers and one monograph on probability theory. He received a Ph.D. from Stanford University.

Reviews for Applied Stochastic Processes

The book under review provides a carefully written and concise introduction to stochastic processes and could be used as lecture notes for a first-year graduate course on stochastic processes with emphasis on applications and computation rather than theoretical aspects. It is especially suited for students in engineering or business who aim for applications in their respective field. ... The book provides many examples and exercises, often of computational nature, from applied probability, for instance queueing theory and stock market models. -Mathematical Reviews, August 2014

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