Ebook: Markov Chains: Models, Algorithms and Applications
- Tags: 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
- Series: International Series in Operations Research & Management Science 83
- Year: 2006
- Publisher: Springer US
- Language: English
- pdf
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.
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:
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
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:
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
....