Quantitative System Risk Assessment From Incomplete Data With Belief Networks and Pairwise Comparison Elicitation

A method for conducting Bayesian elicitation and learning in risk assessment is presented. It assumes that the risk process can be described as a fault tree. This is viewed as a belief network, for which prior distributions on primary event probabilities are elicited by means of a pairwise comparison approach. A novel and fully Bayesian updating procedure, following different observation campaigns of the events in the fault tree for the posterior probabilities assessment, is described. In particular, the goal is to handle contexts where there are limited data information (one of the challenges for elicitation), thus keeping simple the elicitation process and adequately quantifying the uncertainties in the analysis. Often, an important consideration in these contexts is the trade-off between how many of the events in the fault tree can be observed against the information that the extra data yield. How this can be addressed within this method is demonstrated. The application is illustrated through three real examples, including the motivating example of risk assessment of spacecraft explosion during controlled reentry.