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Time to Stationarity for a Continuous-Time Markov Chain

Published online by Cambridge University Press:  27 July 2009

James Allen Fill
Affiliation:
Department of Mathematical Sciences The Johns Hopkins University Baltimore, Maryland 21218

Abstract

Separation is one measure of distance from stationarity for Markov chains. Strong stationary times provide bounds on separation and so aid in the analysis of mixing rates. The precise connection between separation and strong stationary times was drawn by Aldous and Diaconis (1987) (Advances in Applied Mathematics 8: 69−97) for discrete time chains. We develop the corresponding foundational theory for continuous time chains; several new and interesting mathematical issues arise.

Type
Articles
Copyright
Copyright © Cambridge University Press 1991

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References

Aldous, D. & Diaconis, P. (1986). Shuffing cards and stopping times. American Mathematical Monthly 93: 333348.CrossRefGoogle Scholar
Aldous, D. & Diaconis, P. (1987). Strong uniform times and finite random walks. Advances in Applied Mathematics 8: 6997.CrossRefGoogle Scholar
Diaconis, P. (1988). Group representations in probability and statistics. Hayward, California: Institute of Mathematical Statistics.CrossRefGoogle Scholar
Diaconis, P. & Fill, J.A. (1990a). Strong stationary times via a new form of duality. Annals of Probability 18:14831522.CrossRefGoogle Scholar
Diaconis, P. & Fill, J.A. (1990b). Examples for the theory of strong stationary duality with countable state spaces. Probability in the Engineering and Informational Sciences 4: 157180.CrossRefGoogle Scholar
Fill, J.A. (1991a). Eigenvalue bounds on convergence to stationarity for nonreversible Markov chains, with an application to the exclusion process. Annals of Applied Probability 1 (in press).CrossRefGoogle Scholar
Fill, J.A. (1991b). Strong stationary duality for continuous-time Markov chains. Part I.: theory, Journal of Theoretical Probability. (in press).CrossRefGoogle Scholar
Fill, J.A. (1991c). Strong stationary duality for continuous-time Markov chains; Part II: applications. Manuscript in preparation.Google Scholar
Griffeath, D. (1975). A maximal coupling for Markov chains. Zeitschrift für Wahrscheinlichkeitstheorie und Verwunde Gebiete 31: 95100.CrossRefGoogle Scholar
Jerrum, M. & Sinclair, A. (1989). Approximate counting, uniform generation and rapidly mixing Markov chains. Information and Computation 82: 93133.Google Scholar
Lamperti, J. (1977). Stochastic processes. New York: Springer-Verlag.CrossRefGoogle Scholar
Matthews, P. (1987). Mixing rates for a random walk on the cube. SIAM Journal on Algebraic and Discrete Methods 8: 746752.CrossRefGoogle Scholar
Mihail, M. (1989). Combinatorial aspects of expanders. PhD dissertation, Harvard University.Google Scholar
Rudin, W. (1974). Real and complex analysis. New York: McGraw-Hill.Google Scholar
Thorisson, H. (1988). Future independent times and Markov chains. Probability Theory and Related Fields 78: 143148.CrossRefGoogle Scholar