Abstract
Modeling Bayesian networks manually is often a tedious task. This paper presents a methodological view onto the effective modeling of Bayesian networks. It features intuitive techniques that are especially suited for inexperienced users: We propose a process model for the modeling task, and discuss strategies for acquiring the network structure. Furthermore, we describe techniques for a simplified construction of the conditional probability tables using constraints and a novel extension of the Ranked-Nodes approach. The effectiveness and benefit of the presented approach is demonstrated by three case studies.
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Atzmueller, M., Lemmerich, F. (2008). A Methodological Approach for the Effective Modeling of Bayesian Networks. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds) KI 2008: Advances in Artificial Intelligence. KI 2008. Lecture Notes in Computer Science(), vol 5243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85845-4_20
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DOI: https://doi.org/10.1007/978-3-540-85845-4_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85844-7
Online ISBN: 978-3-540-85845-4
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