Markov Chains: Models, Algorithms and Applications

✍ Scribed by Wai-Ki Ching, Michael K. Ng (auth.)

Publisher: Springer US
Year: 2006
Tongue: English
Leaves: 211
Series: International Series in Operations Research & Management Science 83
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages I-XIV
Introduction....Pages 1-35
Queueing Systems and the Web....Pages 37-59
Re-manufacturing Systems....Pages 61-75
Hidden Markov Model for Customers Classification....Pages 77-85
Markov Decision Process for Customer Lifetime Value....Pages 87-109
Higher-order Markov Chains....Pages 111-139
Multivariate Markov Chains....Pages 141-169
Hidden Markov Chains....Pages 171-189
Back Matter....Pages 191-208

✦ Subjects

Probability Theory and Stochastic Processes; Operations Research/Decision Theory; Mathematical Modeling and Industrial Mathematics; Production/Logistics; Probability and Statistics in Computer Science; Math Applications in Computer Scien

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