Markov chains : models, algorithms and applications

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 disc...

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Auteurs principaux: Ching, Wai Ki, 1969-, Ng, Michael K., 19..- (Auteur), Ng, Michael K. (Auteur)
格式: Livre numérique
語言:Anglais
出版: New York, NY : Springer US [20..].
Cham : Springer Nature
版:1st ed. 2006.
叢編:International Series in Operations Research & Management Science 83
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提示: Description d'après consultation du 08 avril 2011
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Edition sous un autre format:• Markov chains, models, algorithms and applications, Wai-Ki Ching, Michael K. Ng, New York, Springer, 2006, 1 vol. (XIV-205 p.), International series in operations research & management science, 0-387-29335-3
• Markov chains, models, algorithms and applications, Wai-Ki Ching, Michael K. Ng, New York, Springer, 2010, 1 v.(x-201p.), International series in operations research & management science, 978-1-441-93986-9
• Markov Chains: Models, Algorithms and Applications, Texte imprimé, 9780387510095
• Markov chains, models, algorithms and applications, Wai-Ki Ching, Michael K. Ng, New York, Springer, 2006, 1 vol. (XIV-205 p.), International series in operations research & management science, 0-387-29335-3
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505 1 |a Queueing Systems and the Web Re-manufacturing Systems Hidden Markov Model for Customers Classification Markov Decision Process for Customer Lifetime Value Higher-order Markov Chains Multivariate Markov Chains Hidden Markov Chains 
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520 |a 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 
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