Abstract
A class of iterative aggregation/disaggregation methods (IAD) for computation of some important characteristics of Markov chains such as stationary probability vectors and mean first passage times matrices is presented and convergence properties of the corresponding algorithms are analyzed. Particular attention is focused on the fast convergence. Some classes of problems are identified for which the IAD methods return exact solutions after one single iteration sweap.
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