Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods

Braham Hayette (), Berdjoudj Louiza (), Boualem Mohamed () and Rahmania Nadji ()
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Braham Hayette: Laboratory of Applied Mathematics, Faculty of Exact Sciences, University of Bejaia, 06000Bejaia, Algeria
Berdjoudj Louiza: Research Unit LaMOS (Modeling and Optimization of Systems), Faculty of Exact Sciences, University of Bejaia, 06000Bejaia, Algeria
Boualem Mohamed: Research Unit LaMOS (Modeling and Optimization of Systems), Faculty of Technology, University of Bejaia, 06000Bejaia, Algeria
Rahmania Nadji: Paul Painleve-UMR Laboratory CNRS 8524, University of Lille, Lille, France

Monte Carlo Methods and Applications, 2019, vol. 25, issue 2, 147-154

Abstract: The stationary distribution is the key of any queueing system; its determination is sufficient to infer the corresponding characteristics. This paper deals with the Er/M/1{\mathrm{Er}/M/1} queue. Bayesian inference is developed to estimate the system parameters, specially the root of the relative equation which allows the determination of the stationary distribution. A numerical study is performed with MCMC methods to support the results, and a comparison with another existing method in the literature (moments method) is provided.

Keywords: Bayesian estimation; stationary distribution; system characteristics; MCMC methods (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/mcma-2019-2035

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