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Approach to Stationarity of the Bernoulli–Laplace Diffusion Model

Part of: Probability theory on algebraic and topological structures Markov processes

Published online by Cambridge University Press:  01 July 2016

Peter Donnelly ,
Peter Lloyd  and
Aidan Sudbury
Peter Donnelly*
Affiliation:
Queen Mary and Westfield College, London
Peter Lloyd*
Affiliation:
Monash University
Aidan Sudbury*
Affiliation:
Monash University
*
* Postal address: School of Mathematical Sciences, Queen Mary and Westfield College, University of London, Mile End Road, London El 4NS, UK.
** Postal address: Department of Physics and Mathematics, Monash University, Clayton, VIC 3168, Australia.
** Postal address: Department of Physics and Mathematics, Monash University, Clayton, VIC 3168, Australia.
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Abstract

Two urns initially contain r red balls and n – r black balls respectively. At each time epoch a ball is chosen randomly from each urn and the balls are switched. Effectively the same process arises in many other contexts, notably for a symmetric exclusion process and random walk on the Johnson graph. If Y(·) counts the number of black balls in the first urn then we give a direct asymptotic analysis of its transition probabilities to show that (when run at rate (n – r)/n in continuous time) for as n →∞, where π n denotes the equilibrium distribution of Y(·) and γ α = 1 – α /β (1 – β). Thus for large n the transient probabilities approach their equilibrium values at time log n + log|γ α | (≦log n) in a particularly sharp manner. The same is true of the separation distance between the transient distribution and the equilibrium distribution. This is an explicit analysis of the so-called cut-off phenomenon associated with a wide variety of Markov chains.

Keywords

RANDOM WALKEXCLUSION PROCESSSEPARATION DISTANCEDISTANCE TRANSITIVE GRAPHJOHNSON GRAPHEBERLEIN POLYNOMIALS

MSC classification

Primary: 60B15: Probability measures on groups or semigroups, Fourier transforms, factorization
Secondary: 60J20: Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.)
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 26 , Issue 3 , September 1994 , pp. 715 - 727
Copyright
Copyright © Applied Probability Trust 1994 

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