A Methodological Approach for the Effective Modeling of Bayesian Networks

Authors:
Martin Atzmueller;Florian Lemmerich
Affiliations:
Department of Computer Science VI Am Hubland, University of Würzburg, Würzburg, Germany 97074;Department of Computer Science VI Am Hubland, University of Würzburg, Würzburg, Germany 97074
Venue:
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Year:
2008

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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.