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Applied Stochastic Processes, by Ming Liao
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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.
- Sales Rank: #3894184 in Books
- Published on: 2013-07-22
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .80" w x 6.00" l, .0 pounds
- Binding: Hardcover
- 208 pages
Review
"The book provides a good background for a first course on stochastic processes as it covers the main topics and contains many examples."
―Zentralblatt MATH 1281
"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
About the Author
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.
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