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...

This introduction to Bayesian statistics presents the main concepts as well as the principal reasons advocated in favour of a Bayesian modelling. We cover the various approaches to prior determination as well as the basis asymptotic arguments in favour of using Bayes estimators. The testing...

Within the Bayesian paradigm, expert knowledge is typically used to construct informative priors, with an emphasis on combination with empirical data to form posterior estimates. In pioneering research, however, the initial step of represent- ing the current state of knowledge may deserve more...