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This book presents an algebraic development of the theory of countable state space Markov chains with discrete and continuous time parameters.
- Sales Rank: #4543368 in Books
- Published on: 1997-01-01
- Released on: 1997-01-01
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .80" w x 6.10" l, 1.26 pounds
- Binding: Hardcover
- 341 pages
From the Back Cover
Markov Processes for Stochastic Modeling presents a review of the author's more recent work in this active area of applied probability, together with an indication of where it links to established research. The book presents an algebraic development of the theory of countable state space Markov chains with discrete and continuous time parameters. The emphasis is on time-dependent behavior, including first passage times of Markov chains. The book discusses measures of the speed of convergence, an algebraic discussion of monotone Markov chains and recent developments of quasi-stationary distributions. These features are complemented by numerous examples drawn from queueing, reliability and other models. The book will be of particular interest to researchers in applied probability, mathematics, telecommunications, econometrics, genetics, epidemiology and electronic engineering, and will prove invaluable as a course text for graduates studying stochastic processes and stochastic modeling.
Most helpful customer reviews
8 of 11 people found the following review helpful.
It is confusing but could help
By A Customer
It is the worst book about stochastic processes I've ever read, it's confusing, if there were concepts clear in your mind after reading the book there won't be anymore. It is a very useful book to burn.
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