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Yaglom limit for stochastic fluid models

Part of: Theory of computing Discrete mathematics in relation to computer science

Published online by Cambridge University Press:  08 October 2021

Nigel G. Bean ,
Małgorzata M. O’Reilly Open the ORCID record for Małgorzata M. O’Reilly [Opens in a new window]  and
Zbigniew Palmowski Open the ORCID record for Zbigniew Palmowski [Opens in a new window]
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]
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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.

Keywords

Stochastic fluid modelMarkov chainLaplace–Stieltjes transformYaglom limitlimiting conditional distribution

MSC classification

Primary: 68Q25: Analysis of algorithms and problem complexity 68R10: Graph theory (including graph drawing)
Secondary: 68U05: Computer graphics; computational geometry
Type
Original Article
Information
Advances in Applied Probability , Volume 53 , Issue 3 , September 2021 , pp. 649 - 686
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Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of Applied Probability Trust

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