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Decay rates and cutoff for convergence and hitting times of Markov chains with countably infinite state space

Published online by Cambridge University Press:  01 July 2016

Servet Martínez*
Affiliation:
Universidad de Chile
Bernard Ycart*
Affiliation:
Université René Descartes–Paris V
*
Postal address: Centro Modelamiento Matemático, Universidad de Chile, UMR 2071-CNRS, Casilla 170/3, Santiago, Chile. Email address: [email protected]
∗∗ Postal address: Math–Info, 45 rue des Saints-Pères 75270, Paris Cedex 06, France.

Abstract

For a positive recurrent continuous-time Markov chain on a countable state space, we compare the access time to equilibrium to the hitting time of a particular state. For monotone processes, the exponential rates are ranked. When the process starts far from equilibrium, a cutoff phenomenon occurs at the same instant, in the sense that both the access time to equilibrium and the hitting time of a fixed state are equivalent to the expectation of the latter. In the case of Markov chains on trees, that expectation can be computed explicitly. The results are illustrated on the M/M/∞ queue.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2001 

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References

[1] Aldous, D and Fill, J. (1999). Reversible Markov Chains in Random Walks and Graphs. In preparation. Available at http://www.stat.berkeley.edu/simaldous/book.html.Google Scholar
[2] Anderson, W. J. (1991). Continuous-time Markov Chains. An Applications-Oriented Approach. Springer, New York.Google Scholar
[3] Bouleau, N. and Lépingle, D. (1994). Numerical Methods for Stochastic Processes. John Wiley, New York.Google Scholar
[4] Cavender, J. A. (1978). Quasi-stationary distributions of birth and death processes. Adv. Appl. Prob. 10, 570586.Google Scholar
[5] Çinlar, E., (1975). Introduction to Stochastic Processes. Prentice Hall, New York.Google Scholar
[6] Diaconis, P. (1996). The cutoff phenomenon in finite Markov chains. Proc. Nat. Acad. Sci. USA 93, 16591664.CrossRefGoogle ScholarPubMed
[7] Down, D., Meyn, S. P. and Tweedie, R. L. (1995). Exponential and uniform ergodicity of Markov processes. Ann. Prob. 23, 16711691.Google Scholar
[8] Feller, W. (1971). An Introduction to Probability Theory and its Applications, Vol. II. John Wiley, London.Google Scholar
[9] Ferrari, P. A., Kesten, H., Martínez, S. and Picco, P. (1995). Existence of quasi-stationary distributions. A renewal dynamical approach. Ann. Prob. 23, 501521.Google Scholar
[10] Ferrari, P. A., Martínez, S. and Picco, P. (1992). Existence of non-trivial quasi-stationary distributions in the birth–death chain. Adv. Appl. Prob. 24, 795813.Google Scholar
[11] Gray, L., Béguin, M. and Ycart, B. (1998). The load transfer model. Ann. Appl. Prob. 8, 337353.Google Scholar
[12] Horn, R. A. and Johnson, C. R. (1985). Matrix Analysis. Cambridge University Press.Google Scholar
[13] Jacka, S. D. and Roberts, G. O. (1995). Weak convergence of conditioned processes on a countable state space. J. Appl. Prob. 32, 902916.Google Scholar
[14] Kamae, T., Krengel, U. and O'Brien, G. L. (1977). Stochastic inequalities on partially ordered spaces. Ann. Prob. 5, 899912.Google Scholar
[15] Keilson, J. and Ramaswamy, R. (1984). Convergence of quasi-stationary distributions in birth–death processes. Stoch. Proc. Appl. 18, 301312.CrossRefGoogle Scholar
[16] Kelly, F. P. (1979). Reversibility and Stochastic Networks, John Wiley, London.Google Scholar
[17] Kijima, M and Seneta, E. (1991). Some results for quasi-stationary distributions of birth–death processes. J. Appl. Prob. 28, 502511.Google Scholar
[18] Kijima, M. (1992). Evaluation of the decay parameter for some specialized birth–death processes. J. Appl. Prob. 29, 781791.CrossRefGoogle Scholar
[19] Kingman, J. F. C. (1963). Ergodic properties of continuous-time Markov processes and their discrete skeletons. Proc. London Math. Soc. 13, 593604.CrossRefGoogle Scholar
[20] Lindvall, T. (1992). Lectures on the Coupling Methods. John Wiley, New York.Google Scholar
[21] López, J., Martínez, S. and Sanz, G. (2000). Stochastic domination and Markovian couplings. Adv. Appl. Prob. 32, 10641076.Google Scholar
[22] Massey, A. W. (1987). Stochastic orderings for Markov processes on partially ordered spaces. Math. Operat. Res. 12, 350367.Google Scholar
[23] Meyn, S. P. and Tweedie, R. L. (1993). Markov Chains and Stochastic Stability. Springer, London.Google Scholar
[24] Nair, N. and Pollett, P. (1993). On the relationship between μ-invariant measures and quasistationary distributions for continuous Markov chains. Adv. Appl. Prob. 25, 82102, 717–719.Google Scholar
[25] Popov, N. N.. (1977). Conditions for geometric ergodicity of countable Markov chains. Soviet Math. Dokl. 18, 676679.Google Scholar
[26] Saloff-Coste, L.. (1997). Lectures on finite Markov chains. In Lectures on Probability Theory and Statistics (Lecture Notes Math. 1665), eds Giné, E., Grimmett, G. R. and Saloff-Coste, L.. Springer, Berlin, pp. 301413.CrossRefGoogle Scholar
[27] Van Doorn, E. (1991). Quasi-stationary distributions and convergence to quasi-stationarity of birth–death processes. Adv. Appl. Prob. 23, 683700.CrossRefGoogle Scholar
[28] Van Doorn, E. and Schrijner, P. (1995). Geometric ergodicity and quasi-stationarity in discrete-time birth–death processes. J. Austal. Math. Soc. B 37, 121144.Google Scholar