Ubiquinol oxidase (H+-transporting)

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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.[1][2] Such models are often described as M/G/1 type Markov chains because they can describe transitions in an M/G/1 queue.[3][4] The method is a more complicated version of the matrix geometric method and is the classical solution method for M/G/1 chains.[5]

Method description

An M/G/1-type stochastic matrix is one of the form[3]

P=(B0B1B2B3A0A1A2A3A0A1A2A0A1)

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.[6][7] If P is irreducible and positive recurrent then the stationary distribution is given by the solution to the equations[3]

Pπ=π and eTπ=1

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[3]

G=i=0GiAi.

G is called the auxiliary matrix.[8] Matrices are defined[3]

Ai+1=j=i+1Gji1AjBi=j=iGjiBj

then π0 is found by solving[3]

B0π0=π0(eT+eT(Ii=1Ai)1i=1Bi)π0=1

and the πi are given by Ramaswami's formula,[3] a numerically stable relationship first published by Vaidyanathan Ramaswami in 1988.[9]

πi=(IA1)1[Bi+1π0+j=1i1Ai+1jπj],i1.

Computation of G

There are two popular iterative methods for computing G,[10][11]

Tools

References

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Template:Queueing theory

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