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). Proposed solution is to develop a method to systemize the way expert driven causal BN 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. The method involves a new way of framing problems and extensions to BN theory, notation and tools. As the work complements current data-driven approaches, it will lead to improved BN modelling both when there is extensive data as well as none.
- project number: ERC-2013-AdG339182-BAYES_KNOWLEDGE
- funding – FP7-IDEAS-ERC
- project duration – 4/2014-3/2018
- role – Bayesian network programmer
- my participation – 4/2015-3/2018 (part-time)
Constantinou A.C., Fenton N., Marsh W., Radlinski L., From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support, Artificial Intelligence in Medicine, vol. 67, pp. 75-93, 2016.