Hostname: page-component-5cf477f64f-pw477 Total loading time: 0 Render date: 2025-04-01T00:37:57.787Z Has data issue: false hasContentIssue false
  • English
  • Français

Strong ergodicity for continuous-time, non-homogeneous Markov chains

Published online by Cambridge University Press:  14 July 2016

Mark Scott ,
Barry C. Arnold  and
Dean L. Isaacson
Mark Scott*
Affiliation:
Mayo Clinic
Barry C. Arnold*
Affiliation:
University of California, Riverside
Dean L. Isaacson*
Affiliation:
Iowa State University
*
∗Postal address: Department of Oncology, Mayo Clinic, Rochester, MN 55901, U.S.A.
∗∗Postal address: Department of Statistics, University of California, Riverside, CA 92501, U.S.A.
∗∗∗Postal address: Department of Statistics, Iowa State University, Ames, IA 50011, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

Characterizations of strong ergodicity for Markov chains using mean visit times have been found by several authors (Huang and Isaacson (1977), Isaacson and Arnold (1978)). In this paper a characterization of uniform strong ergodicity for a continuous-time non-homogeneous Markov chain is given. This extends the characterization, using mean visit times, that was given by Isaacson and Arnold.

Keywords

MARKOV CHAINNON-HOMOGENEOUSCONTINUOUS TIMESTRONG ERGODICITY
Type
Short Communications
Information
Journal of Applied Probability , Volume 19 , Issue 3 , September 1982 , pp. 692 - 694
Copyright
Copyright © Applied Probability Trust 1982 

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

Huang, C. and Isaacson, D. (1977) Ergodicity using mean visit times. J. London Math. Soc. 14, 570–576.Google Scholar
Isaacson, D. and Arnold, B. (1978) Strong ergodicity for continuous-time Markov chains. J. Appl. Prob. 15, 699–706.CrossRefGoogle Scholar
Scott, M. (1979) Characterizations of Strong Ergodicity for Continuous-Time Markov Chains. Ph.D. dissertation, Iowa State University, Ames, Iowa.CrossRefGoogle Scholar