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＜電子ブック＞
Markov Chains: Models, Algorithms and Applications / by Wai-Ki Ching, Michael K. Ng
(International Series in Operations Research & Management Science. ISSN:22147934 ; 83)

版	1st ed. 2006.
出版者	New York, NY : Springer US : Imprint: Springer
出版年	2006
本文言語	英語
大きさ	XIV, 208 p. 18 illus : online resource
著者標目	*Ching, Wai-Ki author Ng, Michael K author SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Operations research LCSH:Mathematical models LCSH:Production management LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Mathematical statistics FREE:Probability Theory FREE:Operations Research and Decision Theory FREE:Mathematical Modeling and Industrial Mathematics FREE:Operations Management FREE:Probability and Statistics in Computer Science FREE:Mathematical Applications in Computer Science
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/0-387-29337-X

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