Single server queueing model with Gumbel distribution using Bayesian approach

Year 2016, Volume: 45 Issue: 4, 1275 - 1296, 01.08.2016

A. Jabarali K. Senthamarai Kannan

Abstract

Bayesian methodology is an important technique in statistics, and
especially in mathematical statistics.
It consists of the sample
information along with the prior information available about the
parameter before the sample has been observed. This paper exhibits
the estimation of the parameters of queueing model with inter-arrival
time and service time which follows Gumbel distribution. Bayesian
procedure is applied to obtain the estimation of the model parameters
and the trac intensity of queueing model based on the informative
and the non-informative prior knowledges. In this paper, the Bayesian
estimates are carried out by numerically and graphically with the
help of Markov Chain Monte Carlo (MCMC) simulation technique,
particularly in Gibbs sampling algorithm.

Keywords

Queue, Gumbel distribution, Bayesian estimation, Gibbs sampling, Markov Chain Monte Carlo technique

References

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