Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-23T13:48:47.639Z Has data issue: false hasContentIssue false

Conditions for strong ergodicity using intensity matrices

Published online by Cambridge University Press:  14 July 2016

Jean Johnson*
Affiliation:
University of Kansas
Dean Isaacson*
Affiliation:
Iowa State University
*
Postal address: Department of Mathematics, University of Kansas, Lawrence, KS 66045, USA.
∗∗ Postal address: Department of Statistics, Iowa State University, Ames, IA 50011, USA.

Abstract

Sufficient conditions for strong ergodicity of discrete-time non-homogeneous Markov chains have been given in several papers. Conditions have been given using the left eigenvectors ψn of Pn(ψ nPn = ψ n) and also using the limiting behavior of Pn. In this paper we consider the analogous results in the case of continuous-time Markov chains where one uses the intensity matrices Q(t) instead of P(s, t). A bound on the rate of convergence of certain strongly ergodic chains is also given.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1988 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Anily, S. and Federgruen, A. (1985) Ergodicity in parametric non-stationary Markov chains: an application to simulated annealing methods. Operat. Res. To appear.Google Scholar
[2] Anily, S. and Federgruen, A. (1987) Simulated annealing methods with general acceptance probabilities. J. Appl. Prob. 24, 657667.CrossRefGoogle Scholar
[3] Cinlar, E. (1975) Introduction to Stochastic Processes. Prentice-Hall, Englewood Cliffs, N.J.Google Scholar
[4] Gidas, B. (1985) Nonstationary markov chains and convergence of the annealing algorithm. J. Statist. Phys. 39, 73131.CrossRefGoogle Scholar
[5] Griffeath, D. (1975) Uniform coupling of nonhomogeneous Markov chains. J. Appl. Prob. 12, 753763.Google Scholar
[6] Huang, C., Isaacson, D. and Vinograde, B. (1976) The rate of convergence of nonhomogeneous Markov chains. Z. Wahrscheinlichkeitsth. 35, 141146.Google Scholar
[7] Iosifescu, M. (1980) Finite Markov Processes and Their Applications. Wiley, Bucharest.Google Scholar
[8] Isaacson, D. and Madsen, R. (1976) Markov Chains: Theory and Applications. Wiley, New York.Google Scholar
[9] Johnson, J. (1984) Ergodic Properties of Nonhomogeneous, Continuous-time Markov Chains. Ph.D. Dissertation, Iowa State University.Google Scholar
[10] Madsen, R. and Isaacson, D. (1973) Strongly ergodic behavior for nonstationary Markov processes. Ann. Prod. 1, 329335.Google Scholar
[11] Mitra, D., Romeo, F. and Sangiovanni-Vincentelli, A. (1986) Convergence and finite-time behavior of simulated annealing. Adv. Appl. Prob. 18, 747771.Google Scholar
[12] Mott, J. L. (1957) Conditions for ergodicity of non-homogeneous, finite Markov chains. Proc. R. Soc. Edinburgh 64, 369380.Google Scholar
[13] Paz, A. P. (1971) Introduction to Probabilistic Automata. Academic Press, New York.Google Scholar