An Introduction to Stochastic Processes
An Introduction to Stochastic Processes by Edward P.C. Kao, published by Courier Dover Publications on December 18, 2019, is a comprehensive resource designed for advanced undergraduates and beginning graduate students. This 448-page book adopts an applications- and computer-oriented approach to teaching stochastic processes, diverging from the traditional formal and mathematically rigorous methods. It covers essential topics such as Poisson processes, Markov chains, and Brownian motion, making it relevant for fields like operations research, finance, and applied mathematics.
Readers will find that the book begins with a review of preliminary materials, including transform methods and basic concepts in mathematical analysis. Each chapter delves into specific stochastic processes and concludes with problems, bibliographic notes, and references, enhancing the learning experience. The inclusion of a Solutions Manual available to instructors further supports the educational framework. This edition is presented in English and is suitable for those with a background in intermediate calculus, linear algebra, and introductory probability.
Official synopsis Publisher
A vigorous response to the challenges of incorporating computer use into the teaching and learning of stochastic processes, this book takes an applications- and computer-oriented approach rather than the standard formal and mathematically rigorous approach. It is suitable for advanced undergraduates and beginning graduate students in operations research, management science, finance, engineering, statistics, computer science, and applied mathematics. Prerequisites are intermediate-level calculus, elementary linear algebra, and an introductory course in probability with an emphasis in operational skills on conditioning.
The first chapter reviews some preliminary materials, including transform methods and basic concepts in mathematical analysis. Subsequent chapters explore variants of Poisson processes, renewal processes, discrete-time and continuous-time Markov chains, Markov renewal and semi-regenerative processes, and Brownian motion and other diffusion processes. Each chapter concludes with problems, bibliographic notes, references, and an Appendix. A Solutions Manual is available to instructors upon request.
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