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Light-tailed asymptotics of the stationary probability vectors of Markov chains of GI/G/1 type. (English) Zbl 1099.60061
The paper deals with the light-tailed asymptotic behavior of stationary probability vectors of block-structured Markov chains. It presents a novel approach to evaluating the light-tailed asymptotics by means of the RG-factorization of both the repeating blocks and the Wiener-Hopf equations for the boundary blocks of the transition probability matrix, the RG-factorization plays a role similar to that played by the Wiener-Hopf factorization in analyzing waiting times. The stationary probability vector is partitioned into vectors (π 0 ,π 1 ,π 2 ,...), {π k } are expressed in terms of the R-measure and, finally, in terms of the blocks in the transition probability matrix of GI/G/1 type. This expression can be used to show that {π k } is light-tailed under certain condition. The paper explicitly presents the tail-asymptotics of {π k }. There are defined three classes of sequences of nonnegative matrices, two of them exhibit light-tailed asymptotics, the third heavy-tailed one. The classification of {π k } is discussed in terms of the classification of the repeating row and the boundary row.
MSC:
60K25Queueing theory
60K15Markov renewal processes
60J22Computational methods in Markov chains
90B22Queues and service (optimization)