  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 Bayesian updating procedure, following observation of some or all
of the events in the fault tree, is described. The application is illustrated
through the motivating example of risk assessment of spacecraft explosion
during controlled re-entry.
