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Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. "Excellent introduction." — Journal of the American Statistical Association. Bibliography. 1970 edition.
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Raymond Dzik
reviewed Applied Probability Models with Optimization Applications on December 28, 2015
Applied Probability Models with Optimization Applications
An absolutely essential book for representation theory. Well translated etc, the only thing I would say is a fair few of the proofs are needlessly complicated. For instance, I remember early on a proof where we have to prove something for all finite dimensional vector spaces, which one would naturally do by induction starting from a line and working upwards. However, Serre insists on this very obtuse proof in strong induction, starting from the 0 vector space? Very weird.
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