Knowledge engineering and management: the CommonKADS methodology
Knowledge engineering and management: the CommonKADS methodology
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A Practical Guide to Knowledge Acquisition
A Practical Guide to Knowledge Acquisition
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
Probabilities for a probabilistic network: a case study in oesophageal cancer
Artificial Intelligence in Medicine
Ontologies for probabilistic networks: a case study in the oesophageal-cancer domain
The Knowledge Engineering Review
KAMET II: An open diagnosis knowledge-acquisition methodology
Journal of Computational Methods in Sciences and Engineering - Selected papers from the International Conference on Computer Science,Software Engineering, Information Technology, e-Business, and Applications, 2003
A Methodological Approach for the Effective Modeling of Bayesian Networks
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Preventing knowledge transfer errors: Probabilistic decision support systems through the users' eyes
International Journal of Approximate Reasoning
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
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Building a probabilistic network for a real-life application is a difficult and time-consuming task. Methodologies for building such a network, however, are still lacking. Also, literature on network-specific modelling issues is quite scarce. As we have developed a large probabilistic network for a complex medical domain, we have encountered and resolved numerous non-trivial modelling issues. Since many of these issues pertain not only to our application but are likely to emerge for other applications as well, we feel that sharing them will contribute to engineering probabilistic networks in general.