On Bayesian inference for the M/G/1 queue with efficient MCMC sampling

Abstract

We introduce an efficient MCMC sampling scheme to perform Bayesian inference in the M/G/1 queueing model given only observations of interdeparture times. Our MCMC scheme uses a combination of Gibbs sampling and simple Metropolis updates together with three novel "shift" and "scale" updates. We show that our novel updates improve the speed of sampling considerably, by factors of about 60 to about 180 on a variety of simulated data sets.

References in corpus (1)

• Markov Chain Sampling for Non-linear State Space Models Using Embedded Hidden Markov Models

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