Eliciting prior information to enhance the predictive performance of bayesian graphical models

This work focuses on assessment of predictive performance and provides two techniques for improving the predictive performance of Bayesian graphical models and describes a technique for eliciting a prior distribution for competing models from domain experts.

Abstract

Both knowledge-based systems and statistical models are typically concerned with making predictions about future observables. Here we focus on assessment of predictive performance and provide two techniques for improving the predictive performance of Bayesian graphical models. First, we present Bayesian model averaging, a technique for accounting for model uncertainty. Second, we describe a technique for eliciting a prior distribution for competing models from domain experts. We explore the predictive performance of both techniques in the context of a urological diagnostic problem.