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Birth-and-death processes on the integers with phases and general boundaries

Published online by Cambridge University Press:  14 July 2016

Bruce Hajek*
Affiliation:
University of Illinois at Urbana-Champaign
*
Postal address: Coordinated Science Laboratory, 1101 W. Springfield, Urbana, IL 61801, U.S.A.

Abstract

The invariant probability distribution is found for a class of birth-and-death processes on the integers with phases and one or two boundaries. The invariant vector has a matrix geometric form and is found by solving a non-linear matrix equation and then finding an invariant probability distribution on the boundary states. Levy's concept of watching a Markov process in a subset is used to naturally decouple the computation of distributions on the boundary and interior states.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1982 

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Footnotes

This research was supported by the Naval Research Laboratory under Contract U.S. NAVY N00014-80-C-0802.

References

Chung, K. L. (1974) A Course in Probability Theory. Academic Press, New York.Google Scholar
Dynkin, E. B. (1965) Markov Processes, Vol. I. Springer-Verlag, New York.Google Scholar
Freedman, D. (1971) Approximating Countable State Markov Chains. Holden-Day, San Francisco.Google Scholar
Keilson, J. (1979) Markov Chain Models — Rarity and Exponentiality. Springer-Verlag, New York.Google Scholar
Kemeny, J. G., Snell, J. L. and Knapp, A. W. (1976) Denumerable Markov Chains. Springer-Verlag, New York.CrossRefGoogle Scholar
Neuts, M. F. (1978a) The M/M/l queue with randomly varying arrival and service rates. Opsearch 15, 139157.Google Scholar
Neuts, M. F. (1978b) Further results on the M/M/1 queue with randomly varying rates. Opsearch 15, 158168.Google Scholar
Neuts, M. F. (1978C) Markov chains with applications in queueing theory, which have a matrix-geometric invariant probability vector. Adv. Appl. Prob. 10, 185212.Google Scholar
Neuts, M. F. (1980) The probabilistic significance of the rate matrix in matrix-geometric invariant vectors. J. Appl. Prob. 17, 291296.Google Scholar
Neuts, M. F. (1981) Matrix-Geometric Solutions in Stochastic Models — An Algorithmic Approach. The Johns Hopkins University Press, Baltimore, MD.Google Scholar
Nummelin, E. and Tweedie, R. L. (1978) Geometric ergodicity and R -positivity for general Markov chains. Ann. Prob. 6, 404420.Google Scholar
Tweedie, R. L. (1982) Operator-geometric stationary distributions for Markov chains with application to queueing models. Adv. Appl. Prob. 14, 368391.Google Scholar