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Rates of convergence of stochastically monotone and continuous time Markov models

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • G. O. Roberts
  • R. L. Tweedie
<mark>Journal publication date</mark>2000
<mark>Journal</mark>Journal of Applied Probability
Issue number2
Number of pages15
Pages (from-to)359-373
Publication StatusPublished
<mark>Original language</mark>English


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.