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Book
xiv, 563 p. : ill. ; 24 cm.
Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, 4e, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of simple stochastic analysis to realistic problems. New to this edition: * Realistic applications from a variety of disciplines integrated throughout the text, including more biological applications* Plentiful, completely updated problems* Completely updated and reorganized end-of-chapter exercise sets, 250 exercises with answers* New chapters of stochastic differential equations and Brownian motion and related processes* Additional sections on Martingale and Poisson process . Realistic applications from a variety of disciplines integrated throughout the text. . Extensive end of chapter exercises sets, 250 with answers . Chapter 1-9 of the new edition are identical to the previous edition . New! Chapter 10 - Random Evolutions . New! Chapter 11- Characteristic functions and Their Applications.
(source: Nielsen Book Data)9780123814166 20160607
Science Library (Li and Ma)
STATS-218-01
Book
xv, 510 p. : ill. ; 25 cm.
  • Preliminaries. The Poisson Process. Renewal Theory. Markov Chains. Continuous--Time Markov Chains. Martingales. Random Walks. Brownian Motion and Other Markov Processes. Stochastic Order Relations. Poisson Approximations. Answers and Solutions to Selected Problems. Index.
  • (source: Nielsen Book Data)9780471120629 20160528
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.
(source: Nielsen Book Data)9780471120629 20160528
Science Library (Li and Ma)
STATS-218-01