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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, Fourth Edition, 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
Upper division undergraduate and graduate-level courses in stochastic processes and stochastic modeling, offered in statistics and mathematics departments at all major universities
Conditional Probability and Conditional Expectation
Markov Chains: Introduction
The Long Run Behavior of Markov Chains
Continuous Time Markov Chains
Brownian Motion and Related Processes
Characteristic Functions and Their Applications
- No. of pages:
- © Academic Press 2011
- 10th December 2010
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Stanford University and The Weizmann Institute of Science
PRAISE FOR THE SECOND EDITION
"This book is a valuable resource for anyone studying combustion processes." --David L. Liscinsky, United Technologist Research Center, in AIAA JOURNAL
"This is an excellent text-book ... The narrative is clear, careful and detailed but, at the same time, designed to draw (not to bore) the reader in. The main strengths, in my opinion, are the wealth of convincing applications, which are discussed at some, but not too much length after each bit of theoretical development, and the large number of exercises given at the ends of sections, not just at the ends of chapters." --Martin Crowder, University of Surrey, Guildford, in THE STATISTICIAN
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