Markov Chains: Models, Algorithms and Applications
Description
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models.
Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.
Reviews / Votes
From the reviews:
"The authors outline recent developments of Markov chain models . . This book is aimed at students, professionals, practitioners, and researchers in scientific computing and operational research, who are interested in the formulation and computation of queuing and manufacturing systems. It gives a number of useful tools for researchers in real applications . ." (Alexander I. Zejfman, Zentralblatt MATH, Vol. 1089 (15), 2006)
"In this book's . essential notions on Markov chains, hidden Markov models, and Markov decision processes are covered, with special emphasis on iterative methods for solving linear systems. . Each chapter finishes with a short summary and sometimes a selection of open problems. . This book is intended for students and researchers in applied mathematics, scientific computing, and operations research . . Overall, this book offers much interesting and up-to-date material on a wide variety of topics, dealing with finite-space Markov processes." (Jozef L. Teugels, Journal of the American Statistical Association, Vol. 103 (483), September, 2008)
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