Regenerative Analysis and Steady State Distributions for Markov Chains

Winfried K. Grassmann, Michael I. Taksar and Daniel P. Heyman
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Winfried K. Grassmann: University of Saskatchewan, Saskatoon, Saskatchewan
Michael I. Taksar: Florida State University, Tallahassee, Florida
Daniel P. Heyman: Bell Communications Research, Holmdel, New Jersey

Operations Research, 1985, vol. 33, issue 5, 1107-1116

Abstract: We apply regenerative theory to derive certain relations between steady state probabilities of a Markov chain. These relations are then used to develop a numerical algorithm to find these probabilities. The algorithm is a modification of the Gauss-Jordan method, in which all elements used in numerical computations are nonnegative; as a consequence, the algorithm is numerically stable.

Keywords: 567 regenerative analysis; 692 imbedded Markov process (search for similar items in EconPapers)
Date: 1985
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:33:y:1985:i:5:p:1107-1116

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