Bayesian Networks for Expert Systems: Theory and Practical Applications

Bayesian network are widely accepted as models for reasoning with uncertainty. In this chapter we focus on models that are created using domain expertise only. After a short review of Bayesian networks models and common Bayesian network modeling approaches, we will discuss in more detail three applications of Bayesian networks. With these applications, we aim to illustrate the modeling power and flexibility of the Bayesian networks that goes beyond the standard textbook applications. The first network is applied in a system for medical diagnostic decision support. A distinguishing feature of this network is the large amount of variables in the model. The second one involves an application for petrophysical decision support to determine the mineral content of a well based on borehole measurements. This model differs from standard Bayesian networks by its continuous variables and nonlinear relations. Finally, we will discuss an application for victim identification by kinship analysis based on DNA profiles. The distinguishing feature in this application is that Bayesian networks are generated and computed on-the-fly based on case information. Wim Wiegerinck SNN Adaptive Intelligence, Geert Grooteplein 21, 6525 EZ Nijmegen, The Netherlands e-mail: [email protected] Bert Kappen Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 21, 6525 EZ Nijmegen, The Netherlands e-mail: [email protected] Willem Burgers SNN Adaptive Intelligence, Geert Grooteplein 21, 6525 EZ Nijmegen, The Netherlands e-mail: [email protected]