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

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.

Keywords:

• prediction
• Bayesian graphical model
• Bayesian network
• decomposable model
• model uncertainty
• elicitation