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Matrix analytic method

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In probability theory, the matrix analytic method is a technique to compute the stationary probability distribution of a Markov chain with a block-triangular transition matrix.[1] Such models are often described as M/G/1 type Markov chains because they can describe transitions in an M/G/1 queue.[2][3] The method is a more complicated version of the matrix geometric solution method and is the classical solution method for M/G/1 chains.[4]

Method description

An M/G/1-type stochastic matrix is one of the form[2]

where Bi and Ai are k × k matrices. (Note that unmarked matrix entries represent zeroes.) Such a matrix describes the embedded Markov chain in an M/G/1 queue.[5][6] If P is irreducible and positive recurrent then the stationary distribution is given by the solution to the equations[2]

where e represents a vector of suitable dimension with all values equal to 1. Matching the structure of P, π is partitioned to π1, π2, π3, …. To compute these probabilities the column stochastic matrix G is computed such that[2]

G is called the auxiliary matrix.[7] Matrices are defined[2]

then π0 is found by solving[2]

and the πi are given by Ramaswami's formula,[2] a numerically stable relationship first published by Vaidyanathan Ramaswami in 1988.[8]

Computation of G

There are two popular iterative methods for computing G,[9][10]

Tools

References

  1. ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:10.1016/0377-2217(84)90034-1, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with |doi=10.1016/0377-2217(84)90034-1 instead.
  2. ^ a b c d e f g Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:10.1080/15326349708807423, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with |doi=10.1080/15326349708807423 instead.
  3. ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:10.1016/j.peva.2005.07.003, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with |doi=10.1016/j.peva.2005.07.003 instead.
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  5. ^ Bolch, Gunter; Greiner, Stefan; de Meer, Hermann; Shridharbhai Trivedi, Kishor (2006). Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications (2 ed.). John Wiley & Sons, Inc. p. 250. ISBN 0471565253.
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