Numerical Solution of Linear Equations Arising in Markov Chain Models
Daniel P. Heyman, Alyson Reeves
ORSA Journal on Computing · 1989 · Volume 1 · Issue 1
Abstract
We examine several methods for numerically solving linear equations that arise in the study of Markov chains. These methods are Gaussian elimination, state-reduction, closed-form matrix solutions, and some hybrid methods. The emphasis is on moments of first-passage times and times to absorption. We compare the methods on the basis of accuracy and computation. We conclude that state-reduction is the most accurate and that the matrix solutions have the least computation time. INFORMS Journal on Computing , ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.
Keywords
mathematics: systems solution, probability: Markov processes
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