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An Efficient Procedure for Computing Quasi-Stationary Distributions of Markov Chains by Sparse Transition Structure

Part of: Parametric inference Markov processes

Published online by Cambridge University Press:  01 July 2016

P. K. Pollett  and
D. E. Stewart
P. K. Pollett*
Affiliation:
The University of Queensland
D. E. Stewart*
Affiliation:
The Australian National University
*
* Postal address: Department of Mathematics, The University of Queensland, QLD 4072, Australia.
** Postal address: Programme in Advanced Computation, School of Mathematical Sciences, The Australian National University, Canberra, ACT 2601, Australia.
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Abstract

We describe a computational procedure for evaluating the quasi-stationary distributions of a continuous-time Markov chain. Our method, which is an ‘iterative version' of Arnoldi's algorithm, is appropriate for dealing with cases where the matrix of transition rates is large and sparse, but does not exhibit a banded structure which might otherwise be usefully exploited. We illustrate the method with reference to an epidemic model and we compare the computed quasi-stationary distribution with an appropriate diffusion approximation.

Keywords

ARNOLDI ALGORITHMCONTINUOUS-TIME MARKOV CHAIN

MSC classification

Primary: 60J27: Continuous-time Markov processes on discrete state spaces
Secondary: 62F15: Bayesian inference
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 26 , Issue 1 , March 1994 , pp. 68 - 79
Copyright
Copyright © Applied Probability Trust 1994 

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Footnotes

This work was carried out with the support of an Australian Research Council grant and a University of Queensland Special Project Grant.

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