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On the identification of continuous-time Markov chains with a given invariant measure

Published online by Cambridge University Press:  14 July 2016

P. K. Pollett*
Affiliation:
The University of Queensland
*
Postal address: Department of Mathematics, The University of Queensland, QLD 4072, Australia. e-mail: [email protected]

Abstract

In [14] a necessary and sufficient condition was obtained for there to exist uniquely a Q-process with a specified invariant measure, under the assumption that Q is a stable, conservative, single-exit matrix. The purpose of this note is to demonstrate that, for an arbitrary stable and conservative q-matrix, the same condition suffices for the existence of a suitable Q-process, but that this process might not be unique. A range of examples is considered, including pure-birth processes, a birth process with catastrophes, birth-death processes and the Markov branching process with immigration.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1994 

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Footnotes

This work was funded under an Australian Research Council Grant and a University of Queensland Special Project Grant.

References

[1] Anderson, W. J. (1991) Continuous-Time Markov Chains: An Applications Oriented Approach. Springer-Verlag, New York.CrossRefGoogle Scholar
[2] Doob, J. L. (1945) Markov chains-denumerable case. Trans. Amer. Math. Soc. 58, 455473.Google Scholar
[3] Feller, W. (1957) On boundaries and lateral conditions for the Kolmogoroff differential equations, Ann. Math. 65, 527570.Google Scholar
[4] Chen-Ting, Hou and Mufa, Chen (1980) Markov processes and field theory. Kexue Tongbao 25, 807811.Google Scholar
[5] Kelly, F. P. (1983) Invariant measures and the q-matrix. In Probability, Statistics and Analysis , ed. Kingman, J. F. C. and Reuter, G. E. H., London Mathematical Society Lecture Notes Series 79, pp. 143160. Cambridge University Press.Google Scholar
[6] Kendall, D. G. (1956) Some further pathological examples in the theory of denumerable Markov processes. Quart. J. Math. Oxford 7, 3956.CrossRefGoogle Scholar
[7] Kendall, D. G. (1959) Unitary dilations of one-parameter semigroups of Markov transition operators, and the corresponding integral representations for Markov processes with a countable infinity of states. Proc. Lond. Math. Soc. 13, 417431.Google Scholar
[8] Nair, M. G. and Pollett, P. K. (1993) On the relationship between µ-invariant measures and quasi-stationary distributions for continuous-time Markov chains. Adv. Appl. Prob. 25, 82102.Google Scholar
[9] Pakes, A. G. (1993) Absorbing Markov and branching processes with instantaneous resurrection. Stoch. Proc. Appl. 48, 85106.Google Scholar
[10] Pakes, A. G. (1993) Explosive Markov branching processes: entrance laws and limiting behaviour. Adv. Appl. Prob. 25, 737756.Google Scholar
[11] Pakes, A. G. (1995) Quasi-stationary laws for Markov processes: examples of an always proximate absorbing state. Adv. Appl. Prob. 27(1).Google Scholar
[12] Pollett, P. K. (1990) A note on the classification of Q-processes when Q is not regular. J. Appl. Prob. 27, 278290.Google Scholar
[13] Pollett, P. K. (1991) On the construction problem for single-exit Markov chains. Bull. Austral. Math. Soc. 43, 439450.Google Scholar
[14] Pollett, P. K. (1991) Invariant measures for Q-processes when Q is not regular. Adv. Appl. Prob. 23, 277292.Google Scholar
[15] Reuter, G. E. H. (1957) Denumerable Markov processes and the associated contraction semigroups on l . Acta Math. 97, 146.Google Scholar
[16] Reuter, G. E. H. (1959) Denumerable Markov processes (II). J. Lond. Math. Soc. 34, 8191.Google Scholar
[17] Reuter, G. E. H. (1967) Note on resolvents of denumerable submarkovian processes. Z. Wahrscheinlichkeitsth. 9, 1619.Google Scholar