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In probability theory, the matrix analytic method is a technique to compute the stationary probability distribution of a Markov chain which has a repeating structure (after some point) and a state space which grows unboundedly in no more than one dimension. Such models are often described as M/G/1 type Markov chains because they can describe transitions in an M/G/1 queue. The method is a more complicated version of the matrix geometric method and is the classical solution method for M/G/1 chains. ==Method description== An M/G/1-type stochastic matrix is one of the form〔 :: where ''B''''i'' and ''A''''i'' 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. If ''P'' is irreducible and positive recurrent then the stationary distribution is given by the solution to the equations〔 :: where e represents a vector of suitable dimension with all values equal to 1. Matching the structure of ''P'', ''π'' is partitioned to ''π''1, ''π''2, ''π''3, …. To compute these probabilities the column stochastic matrix ''G'' is computed such that〔 :: ''G'' is called the auxiliary matrix. Matrices are defined〔 :: then ''π''0 is found by solving〔 :: and the ''π''''i'' are given by Ramaswami's formula,〔 a numerically stable relationship first published by Vaidyanathan Ramaswami in 1988. :: 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「matrix analytic method」の詳細全文を読む スポンサード リンク
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