Informative Priors and Bayesian Computation

Abstract

The use of prior distributions is often a controversial topic in Bayesian inference. Informative priors are often avoided at all costs. However, when prior information is available informative priors are an appropriate way of introducing this information into the model. Furthermore, informative priors, when used properly and creatively, can provide solutions to computational issues and improve modeling efficiency. Through three examples with different applications we demonstrate the importance and usefulness of informative priors in incorporating external information into the model and overcoming computational difficulties.