Effective Bayesian Modelling with Knowledge before Data

This project aims to improve evidence-based decision-making. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. To address this problem Bayesian analysis, which enables domain experts to supplement observed data with subjective probabilities, is normally used. As real-world problems typically involve multiple uncertain variables, Bayesian analysis is extended using a technique called Bayesian networks (BNs). But, despite many great benefits, BNs have been under-exploited, especially in areas where they offer the greatest potential for improvements (law, medicine and systems engineering). This is mainly because of widespread resistance to relying on subjective knowledge. To address this problem much current research assumes sufficient data are available to make the expert‟s input minimal or even redundant; with such data it may be possible to „learn‟ the underlying BN model. But this approach offers nothing when there is limited or no data. Even when „big‟ data are available the resulting models may be superficially objective but fundamentally flawed as they fail to capture the underlying causal structure that only expert knowledge can provide.