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Markov chains : models, algorithms and applications

	Author(s):	Ching W.-K., Ng M.K.
	Collection:	International series in operations research and management science 83
	Publisher:	Springer
	Year:	2006
	Language:	English
	Pages:	211 pages
	Size:	1.41 MB
	Extension:	PDF

[tab] [content title="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. [/content] [content title="Content"] [/content] [content title="About the author"]Ching W. Tang (Chinese: 鄧青雲) is a Hong Kong-born American physical chemist. He was born in Yuen Long, Hong Kong in 1947. Tang currently is the Doris Johns Cherry Professor in the Chemical Engineering Department, University of Rochester. He also has joint appointments in the Department of Chemistry and the Department of Physics and Astronomy. Tang has joined HKUST Jockey Club Institute for Advanced Study of the Hong Kong University of Science and Technology as the IAS Bank of East Asia Professor since September 2013. [/content] [/tab]

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