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Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory. The treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models, and it develops the methods of probability model-building.
Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine conditional probability and conditional expectation, normal processes and covariance stationary processes, and counting processes and Poisson processes. The text concludes with explorations of renewal counting processes, Markov chains, random walks, and birth and death processes, including examples of the wide variety of phenomena to which these stochastic processes may be applied. Numerous examples and exercises complement every section.
- Sales Rank: #1205235 in eBooks
- Published on: 2015-05-05
- Released on: 2015-05-05
- Format: Kindle eBook
From the Back Cover
Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory. The treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models, and it develops the methods of probability model-building.
Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine conditional probability and conditional expectation, normal processes and covariance stationary processes, and counting processes and Poisson processes. The text concludes with explorations of renewal counting processes, Markov chains, random walks, and birth and death processes, including examples of the wide variety of phenomena to which these stochastic processes may be applied. Numerous examples and exercises complement every section.
Dover (2015) republication of the edition published by Holden-Day, Inc., San Francisco, 1962.
See every Dover book in print at
www.doverpublications.com
About the Author
Emanuel Parzen is Distinguished Professor of Statistics at Texas A&M University. He has been a Fellow of the American Statistical Association since 1963 and was awarded the Samuel L. Wilks Memorial Medal in 1994.
Most helpful customer reviews
1 of 1 people found the following review helpful.
A Great Classic
By Dr. Terrence McGarty
This is a classic in stochastic processes. It is targeted to those who will use the material in practice and it is not a theoretical text. It has excellent material on martingales, Poisson Processes, Wiener processes, and the like. It is dated and I had used it when it first came out. It is useful for anyone working in the area. If one is doing research in the mathematical areas this is not the book for you. This is quite useful for those in engineering, controls and communications, and others areas using random process theory. It covers the basics.
5 of 7 people found the following review helpful.
A well-written classic introduction to stochastic theory
By Kris
The math which goes into stochastic theory is key and fundamental; this book is a very readable introduction to the material. Where most books just have the equations (and typically in the tersest form possible), this book makes an effort to explain what is going on "in words" and through some examples.
If you are in a class or working with material where a firm understanding of stochastic theory is necessary, this is a good book to help you get up to speed. This is definitely not an advanced-level book or completely up-to-date, but the basics haven't changed. If you can find it used, it is cheap, too.
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