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.
A nonmeasure theoretical introduction to stochastic processes for students with a knowledge of calculus and elementary probability. Uses a probabilistic rather than an analytic approach whenever possible, for example describing most processes from a sample path perspective. The second edition includes new chapters on martingales and Poisson random variables, adds more information on a number of topics, and rearranges the order of the discussion. The date of the first edition is not indicated. Annotation c. by Book News, Inc., Portland, Or.
