Markov processes for stochastic modeling
By: Masaaki Kijima.
Edition: 1st ed.
Series: Stochastic modeling series; Stochastic modeling.
Publisher: Chapman & Hall
Published: 1997
Place of publication: London; New York
Language: eng
Number of pages: 341
Subjects: Markov processes.; Stochastic processes.
Description: 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.
Notes: Includes bibliographical references (p. [319]-327) and indexes.