Statistics Bayesian inference for queuing model

Bayesian inference & prediction for queuing model

Amit Choudhury and his collaborator estimate traffic intensity for single server queuing model using Bayesian inference. Bayesian inference is an important technique in mathematical statistics, which uses Bayes' theorem to update the probability for a hypothesis as more evidence or information becomes available. This research works is published in the journal Communications in Statistics - Simulation and Computation.

Figure used under Creative Commons 


This paper is concerned with the problem of estimating traffic intensity, ρ for single server queuing model in which inter-arrival and service times are exponentially distributed (Markovian) using data on queue size (number of customers present in the queue) observed at any random point of time. Here, it is assumed that ρ is unknown but random quantity. Bayes estimator of ρ are derived under squared error loss function assuming two forms of prior information on ρ. The performance of the proposed Bayes estimators is compared with that of the corresponding classical version estimator based on max- imum likelihood principle. The model comparison criterion based on Bayes factor is used to select a suitable prior for Bayesian analysis.