Effects of different prior distributions on the Bayesian predictive inference

Predictive inference is one of the oldest methods of statistical inference and it is based on the observable data. Prior information playsimportant role in the Bayesian predictive inference. Researchers in this field are often subjective to exercise non-informative prior distribution. This study tests the effects of a range of prior distributions on predictive inference for different modelling situations such as linear regression models under normal and Student-t errors. Findings reveal that different choice of priors not only provide different prediction distributions of the future response(s) but also change the location and/or scale or shape parameters of the prediction distributions.