Markov chains in random environment with applications in queuing theory and machine learning

We prove the existence of limiting distributions for a large class of Markov chains on a general state space in a random environment. We assume suitable versions of the standard drift and minorization conditions. In particular, the system dynamics should be contractive on the average with respect to...

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Bibliographic Details
Published in:Stochastic processes and their applications 2021-07, Vol.137, p.294-326
Main Authors: Lovas, Attila, Rásonyi, Miklós
Format: Article
Language:English
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Summary:We prove the existence of limiting distributions for a large class of Markov chains on a general state space in a random environment. We assume suitable versions of the standard drift and minorization conditions. In particular, the system dynamics should be contractive on the average with respect to the Lyapunov function and large enough small sets should exist with large enough minorization constants. We also establish that a law of large numbers holds for bounded functionals of the process. Applications to queuing systems, to machine learning algorithms and to autoregressive processes are presented.
ISSN:0304-4149
1879-209X
DOI:10.1016/j.spa.2021.04.002