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.
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