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Rates of convergence of stochastically monotone and continuous time Markov models

Roberts, G. O. and Tweedie, R. L. (2000) Rates of convergence of stochastically monotone and continuous time Markov models. Journal of Applied Probability, 37 (2). pp. 359-373.

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Abstract

In this paper we give bounds on the total variation distance from convergence of a continuous time positive recurrent Markov process on an arbitrary state space, based on Foster-Lyapunov drift and minorisation conditions. Considerably improved bounds are given in the stochastically monotone case, for both discrete and continuous time models, even in the absence of a reachable minimal element. These results are applied to storage models and to diffusion processes.

Item Type: Article
Journal or Publication Title: Journal of Applied Probability
Uncontrolled Keywords: Stochastic monotonicity ; rates of convergence ; Markov chain ; Markov process
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Lancaster Environment Centre
ID Code: 19367
Deposited By: ep_ss_importer
Deposited On: 20 Nov 2008 14:15
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 15:28
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/19367

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