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Yaglom limit for stochastic fluid models

Published online by Cambridge University Press:  08 October 2021

Nigel G. Bean*
Affiliation:
University of Adelaide
Małgorzata M. O’Reilly*
Affiliation:
University of Tasmania
Zbigniew Palmowski*
Affiliation:
Wrocław University of Science and Technology
*
*Postal address: Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers. School of Mathematical Sciences, University of Adelaide, SA 5005 Australia. Email: [email protected]
**Postal address: Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers. Discipline of Mathematics, University of Tasmania, Hobart TAS 7001, Australia. Email: [email protected]
***Postal address: Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, ul. Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland. Email: [email protected]

Abstract

In this paper we analyse the limiting conditional distribution (Yaglom limit) for stochastic fluid models (SFMs), a key class of models in the theory of matrix-analytic methods. So far, only transient and stationary analyses of SFMs have been considered in the literature. The limiting conditional distribution gives useful insights into what happens when the process has been evolving for a long time, given that its busy period has not ended yet. We derive expressions for the Yaglom limit in terms of the singularity˜$s^*$ such that the key matrix of the SFM, ${\boldsymbol{\Psi}}(s)$, is finite (exists) for all $s\geq s^*$ and infinite for $s<s^*$. We show the uniqueness of the Yaglom limit and illustrate the application of the theory with simple examples.

Type
Original Article
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of Applied Probability Trust

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