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Strong stationary times and eigenvalues

Published online by Cambridge University Press:  14 July 2016

Peter Matthews*
Affiliation:
University of Maryland Baltimore County
*
Postal address: Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD 21228, USA.

Abstract

This note gives a new strong stationary time (SST) for reversible finite Markov chains. A modification of the initial distribution is represented as a mixture of distributions which have eigenvector interpretations, and for which good simple SSTs exist. This provides some insight into the relationship between SSTs and eigenvalues. Connections to duality and the threshold phenomenon are discussed.

Type
Short Communications
Copyright
Copyright © Applied Probability Trust 1992 

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Footnotes

Research supported by the National Security Agency under Grant Number MDA 904-88-H-2014.

References

Aldous, D. and Diaconis, P. (1987) Strong uniform times and finite random walks. Adv. Appl. Math. 8, 6997.Google Scholar
Diaconis, P. (1988) Group Representations in Probability and Statistics. IMS, Hayward, CA.Google Scholar
Diaconis, P. and Fill, J. (1990) Strong stationary times via a new form of duality. Ann. Prob. 18, 14831522.Google Scholar
Fill, J. (1990) Time to stationarity for a continuous-time Markov chain. Preprint.Google Scholar
Keilson, J. (1979) Markov Chain ModelsRarity and Exponentiality. Springer-Verlag, New York.Google Scholar
Matthews, P. (1987) Mixing rates for a random walk on the cube. SIAM J. Algebraic Disc. Meth. 8, 746752.Google Scholar
Sinclair, A. and Jerrum, M. (1989) Approximate counting, uniform generation, and rapidly mixing Markov chains. Inf. Comput. 82, 93133.Google Scholar