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<p/><br></br><p><b> About the Book </b></p></br></br>Well-written and accessible, this classic introductory treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models and develops the methods of probability model-building. 1962 edition.<p/><br></br><p><b> Book Synopsis </b></p></br></br>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.<br>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.<br><p/><br></br><p><b> From the Back Cover </b></p></br></br><p>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.<br>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.<br>Dover (2015) republication of the edition published by Holden-Day, Inc., San Francisco, 1962.<br>See every Dover book in print at<br><b>www.doverpublications.com</b></p><p/><br></br><p><b> About the Author </b></p></br></br>Emanuel Parzen is the author of several highly regarded books on probability theory. He taught at Stanford from 1956 until 1970 and then at SUNY Buffalo, and in 1978 he was named Distinguished Professor at Texas A&M University.

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