Event Diary
CRiSM Seminar - Ann Nicholson (Monash)
Location: A1.01
Ann Nicholson (Monash)
Incorporating expert knowledge when learning Bayesian network structure: Heart failure as a case studyBayesian networks (BNs) are rapidly becoming a leading technology in applied Artificial Intelligence (AI), with medicine one of its most popular application area. Both automated learning of BNs and expert elicitation have been used to build these networks, but the potentially more useful combination of these two methods remain underexplored. In this seminar, I will present a case study of this combination using public-domain data for heart failure. We run an automated causal discovery system (CaMML), which allows the incorporation of multiple kinds of prior expert knowledge into its search, to test and compare unbiased discovery with discovery biased with different kinds of expert opinion. We use adjacency matrices enhanced with numerical and colour labels to assist with the interpretation of the results. These techniques are presented within a wider context of knowledge engineering with Bayesian networks (KEBN).