Introduction.- Queueing systems and the web.- Re-manufacturing systems.- Hidden Markov model for customers classification.- Markov decision process for customer lifetime value.- Higher-order Markov decision process.- Multivariate Markov chains.- Hidden Markov chains.- References.- Index.
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 powerful and widely used tool for analyzing a variety of stochastic systems over time
Systematically discusses all the models beginning with the basic to the more advanced and illustrates each of the models with the most recent and high interest applications and uses