Markov Chains: Models, Algorithms and Applications [electronic resource] / by Wai-Ki Ching, Michael K. Ng.
Contributor(s): Ng, Michael K [author.] | SpringerLink (Online service)Material type: TextSeries: International Series in Operations Research & Management Science: 83Publisher: Boston, MA : Springer US, 2006Description: XIV, 208 p. 18 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780387293370Subject(s): Mathematics | Production management | Operations research | Decision making | Mathematical statistics | Computer science -- Mathematics | Mathematical models | Probabilities | Mathematics | Probability Theory and Stochastic Processes | Operation Research/Decision Theory | Mathematical Modeling and Industrial Mathematics | Operations Management | Probability and Statistics in Computer Science | Math Applications in Computer ScienceAdditional physical formats: Printed edition:: No titleDDC classification: 519.2 LOC classification: QA273.A1-274.9QA274-274.9Online resources: Click here to access online
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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.