Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods
-
Hayette Braham
,
Louiza Berdjoudj
,
Mohamed Boualem
and
Nadji Rahmania
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
Acknowledgements
The authors are pleased to thank the anonymous referees and the editor for their valuable comments and suggestions, which improved the content and the presentation of this paper. Thanks also to Prof. A. Bareche (University of Bejaia) and Dr. M. Cherfaoui (University of Biskra) for their help.
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© 2019 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- A third-order weak approximation of multidimensional Itô stochastic differential equations
- Particle diffusion Monte Carlo (PDMC)
- Random walk on rectangles and parallelepipeds algorithm for solving transient anisotropic drift-diffusion-reaction problems
- Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods
- On solving stochastic differential equations
- On the sample-mean method for computing hyper-volumes
- Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction
Articles in the same Issue
- Frontmatter
- A third-order weak approximation of multidimensional Itô stochastic differential equations
- Particle diffusion Monte Carlo (PDMC)
- Random walk on rectangles and parallelepipeds algorithm for solving transient anisotropic drift-diffusion-reaction problems
- Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods
- On solving stochastic differential equations
- On the sample-mean method for computing hyper-volumes
- Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction
