Yaglom limit for Stochastic Fluid Models

TL;DR

This paper analyzes the Yaglom limit for stochastic fluid models, providing explicit expressions, proving uniqueness, and illustrating applications, thereby extending the understanding of long-term behavior conditioned on the process not ending.

Contribution

It introduces the first analysis of the Yaglom limit for SFMs, deriving explicit formulas and establishing the limit's uniqueness, which was not previously addressed.

Findings

Derived explicit expressions for the Yaglom limit in SFMs.

Proved the uniqueness of the Yaglom limit in this context.

Illustrated the theory with simple, illustrative examples.

Abstract

In this paper we provide the analysis of 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, transient and stationary analyses of the SFMs have been only 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 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, ${\bf\Psi}(s)$, 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.