Abstract
Bayesian networks 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 network 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, which go 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 in terms of 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.
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Wiegerinck, W., Kappen, B., Burgers, W. (2010). Bayesian Networks for Expert Systems: Theory and Practical Applications. In: Babuška, R., Groen, F.C.A. (eds) Interactive Collaborative Information Systems. Studies in Computational Intelligence, vol 281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11688-9_20
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DOI: https://doi.org/10.1007/978-3-642-11688-9_20
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