In Bayesian inference, prior probability models are developed for unknown parameters and these prior beliefs are then updated in light of new data, using Bayes’ Rule, to give posterior probability distributions for the parameters. In a subjective Bayesian approach, prior distributions are elicited using expert knowledge or personal beliefs, and the Bayesian framework provides a powerful means by which to incorporate such information into an inference problem. In instances where little or no substantive prior knowledge is available, the Objective Bayes approach provides a principled method to set parameters of the priors; such that the resulting Bayesian procedures possess good long-run frequency properties (29) (for general discussion of Objective Bayes see (21,22)).
