A subgeometric convergence formula for finite-level M/G/1-type Markov chains: via a block-decomposition-friendly solution to the Poisson equation of the deviation matrix

Pub Date : 2023-12-01 DOI:10.1017/apr.2023.39
Hiroyuki Masuyama, Y. Katsumata, Tatsuaki Kimura
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Abstract

The purpose of this study is to present a subgeometric convergence formula for the stationary distribution of the finite-level M/G/1-type Markov chain when taking its infinite-level limit, where the upper boundary level goes to infinity. This study is carried out using the fundamental deviation matrix, which is a block-decomposition-friendly solution to the Poisson equation of the deviation matrix. The fundamental deviation matrix provides a difference formula for the respective stationary distributions of the finite-level chain and the corresponding infinite-level chain. The difference formula plays a crucial role in the derivation of the main result of this paper, and the main result is used, for example, to derive an asymptotic formula for the loss probability in the MAP/GI/1/N queue.
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有限级M/G/1型马尔可夫链的亚几何收敛公式:通过偏差矩阵泊松方程的块分解友好解决方案
本文的研究目的是给出有限级M/G/1型马尔可夫链取其无穷级极限时,其平稳分布的亚几何收敛公式。本研究使用基本偏差矩阵进行,它是偏差矩阵泊松方程的块分解友好解。基本偏差矩阵为有限级链和相应的无限级链各自的平稳分布提供了差分公式。差分公式在本文主要结果的推导中起着至关重要的作用,并利用本文的主要结果推导了MAP/GI/1/N队列中损失概率的渐近公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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