Bayesian change point estimation in Poisson-based control charts


1 Simpson Centre for Health Services Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney, NSW, 2052, Australia

2 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

3 Discipline of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, 4001, Australia


Precise identification of the time when a process has changed enables process engineers to search for a potential
special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a
Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div>
change, a linear trend and a known multiple number of changes in the Poisson rate. The Markov chain Monte Carlo is
used to obtain posterior distributions of the change point parameters and corresponding probabilistic intervals and
inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that
precise estimates can be obtained when they are used in conjunction with the well-known c-, Poisson exponentially
weighted moving average (EWMA) and Poisson cumulative sum (CUSUM) control charts for different change type
scenarios. We also apply the Deviance Information Criterion as a model selection criterion in the Bayesian context, to
find the best change point model for a given dataset where there is no prior knowledge about the change type in the
process. In comparison with built-in estimators of EWMA and CUSUM charts and ML based estimators, the Bayesian
estimator performs reasonably well and remains a strong alternative. These superiorities are enhanced when
probability quantification, flexibility and generalizability of the Bayesian change point detection model are also