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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.
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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