INFERENCE AND PREDICTIONS FROM POISSON POINT-PROCESSES INCORPORATING EXPERT KNOWLEDGE

We present a Bayesian approach for inference and predictions from nonh omogeneous Poisson point processes. The novel feature of our approach is the use of ''expert knowledge'' or ''engineering information'' on t he mean value function of the process. We describe two scenarios from the field of reliability in which engineering information on the mean value function is available. The first scenario pertains to the predic tion of software failures during the debugging phase. Here expert know ledge is provided by the published empirical experiences of software e ngineers involved with the testing and debugging of several software s ystems. The second scenario pertains to the prediction of defects in a rail segment for which expert knowledge is supplied by an engineering model.