A one-semester text providing a non-measure theoretic introduction to stochastic processes. Emphasizes a probabilistic point-of-view. Provides mathematical and statistical concepts throughout and examines stochastic processes using models from various disciplines, with emphasis on stochastic structures. Presents numerous examples and exercises of varying difficulty, consisting of both theoretical and applied problems.

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A nonmeasure theoretic introduction to stochastic processes. Considers its diverse range of applications and provides readers with probabilistic intuition and insight in thinking about problems. This revised edition contains additional material on compound Poisson random variables including an identity which can be used to efficiently compute moments; a new chapter on Poisson approximations; and coverage of the mean time spent in transient states as well as examples relating to the Gibb's sampler, the Metropolis algorithm and mean cover time in star graphs. Numerous exercises and problems have been added throughout the text.

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