Preserving empirical data in expert built Bayesian networks

Supervisor: Prof Norman Fenton

Research group(s): Risk & Information Management

In the absence of empirical data we rely on expert judgment to build rich causal models. For such models the expert has to provide their own judgment about the probability of a variable conditioned on the parents (causes) of the variable. But in many cases, although we have no direct empirical data to inform these necessary conditional probability distributions we may have solid empirical data about the marginal probabilities of one or may variable or about P(A|B) where B is not a parent of A. While there are a small number of special solutions for this problem there is no generic strategy for ensuring that the empirical data is properly ‘preserved’ in the expert built model. This project will determine such a strategy.