Effective Bayesian Modelling with Knowledge Before Data (Short Name: BAYES-KNOWLEDGE) 

This is a European Research Council Advanced Grant (value 1,572,562 euros for a 4-year programme April 2014-march 2018) awarded to Professor Norman Fenton. The full ERC code is ERC-2013-AdG339182-BAYES_KNOWLEDGE.

The project aims to improve evidence-based decision-making in areas such as 
medicine, law, forensics, and transport. 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.  Our solution is to develop a method to systemize the way expert driven causal (Bayesian Network) models can be built and used effectively either in the absence of data or as a means of determining what future data is really required.  Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems.  The proposed research has the potential to both reduce at source much unnecessary data collection and improve the results of analysis of data that is collected. It has the potential to provide rigorous, rational, auditable, visible and quantified probabilistic arguments to support decision-making and recommendations in areas where currently only ‘gut-feel’ is possible. This could lead to: more rational and defensible strategic policy making by decision makers in government, financial, and other organisations; better medical diagnostics; better understanding of the impact of different types of legal and forensic evidence.  The project will enable scientists, statisticians, medics and lawyers, to be better able to reason about probability and understand the role and limitations of data, making better decisions with less data.

The grant is for 4 years and it buys out 50% of Prof Fenton's time as well as some of the time of colleagues at Queen Mary. The project is also funding 3 postdoctoral research fellows and a part-time programmer.