Applied Stochastic Processes introduces the reader to stochastic
processes with a focus on the applications of the theoretical
results. This text is self-contained and logically organized. It
begins with a review of elementary probability, followed by an
introduction to the most important subjects in the field of
stochastic processes. Topics covered include Gaussian and Markovian
processes, Markov Chains, Weiner and Poisson processes, Brownian
motion, and queuing theory with a special highlight on diffusion
processes. The reader will appreciate the clear definitions,
thoroughly explained examples and interesting notes about the
mathematicians referenced throughout the text. In addition, there
are hundreds of advanced, multi-part problems following each
chapter which enable even a novice of theoretical mathematics to
master the material presented. This textbook evolved from the
author's lecture notes for a graduate-level course on applied
stochastic processes. It is meant for graduate-level students in
electrical engineering, applied mathematics, and notably operations
research.
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