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Truncation approximations of invariant measures for Markov chains

Part of: Markov processes Special processes

Published online by Cambridge University Press:  14 July 2016

R. L. Tweedie
R. L. Tweedie*
Affiliation:
Colorado State University
*
∗Postal address: Department of Statistics, Colorado State University, Fort Collins CO 80523, USA. E-mail address: [email protected]
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Abstract

Let P be the transition matrix of a positive recurrent Markov chain on the integers, with invariant distribution π. If (n)P denotes the n x n ‘northwest truncation’ of P, it is known that approximations to π(j)/π(0) can be constructed from (n)P, but these are known to converge to the probability distribution itself in special cases only. We show that such convergence always occurs for three further general classes of chains, geometrically ergodic chains, stochastically monotone chains, and those dominated by stochastically monotone chains. We show that all ‘finite’ perturbations of stochastically monotone chains can be considered to be dominated by such chains, and thus the results hold for a much wider class than is first apparent. In the cases of uniformly ergodic chains, and chains dominated by irreducible stochastically monotone chains, we find practical bounds on the accuracy of the approximations.

Keywords

Irreducible Markov chainsstationary distributionsinvariant measuresgeometric ergodicityapproximationsstochastic monotonicityperturbations

MSC classification

Primary: 60J10: Markov chains (discrete-time Markov processes on discrete state spaces)
Secondary: 60K05: Renewal theory
Type
Research Papers
Information
Journal of Applied Probability , Volume 35 , Issue 3 , September 1998 , pp. 517 - 536
Copyright
Copyright © Applied Probability Trust 1998 

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Footnotes

Work supported in part by NSF Grant DMS-9504561.

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