Model Reduction of Irreducible Markov Chains
نویسندگان
چکیده
We are interested in developing computational tools for reducing the state space of irreducible Markov chains. As means of decreasing the dimensionality of a given Markov chain we study the concept of aggregation. The approximation error between the original and the reduced order model is captured by a metric that penalizes the asymptotic deviation of the outputs of the two systems. For the case of nearly completely decomposable Markov chains we demonstrate how a decomposition approach can be used to derive a low order model of good fidelity.
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تاریخ انتشار 2008