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Some algorithms utilizing iterative aggregation/disaggregation techniques (IAD) are shown to be suitable for computing stationary probability vectors of Markov chains (MC) as well
as other MC-characteristics. The effciency of IAD compared with other methods grows
with growing index of cyclicity of appropriate transition matrices.
The effciency of IAD is
superior for massively cyclic MC's.
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