Email this article E-Bayesian estimation of traffic intensity in a M/M/1 system using expected posterior risk criteria
Abstract
The strategy of E-Bayesian estimation for traffic intensity in a queuing M/M/1 system is developed under different loss functions. The Bayesian and E-Bayesian estimators are derived using a power prior density of traffic intensity and a robust prior for the hyperparameter of the prior distribution. The posterior risk of Bayesian estimators and the associated expected posterior risks of traffic intensity are computed for comarision purposes. A Monte Carlo simulation is conducted for performace analysis of the proposed E-Bayesian estimators using expected posterior criteria.
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