An Assessment of the Effects of Prior Distributions on the Bayesian Predictive Inference

Abstract

Predictive inference is one of the oldest methods of statistical analysis and it is based on observable data. Prior information plays an important role in the Bayesian methodology. Researchers in this field are often subjective to exercise noninformative prior. This study tests the effects of a range of prior distributions on the Bayesian 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.