Abstract
In this paper, we describe the Hugin Tool as an efficient tool for knowledge discovery through construction of Bayesian networks by fusion of data and domain expert knowledge. The Hugin Tool supports structural learning, parameter estimation, and adaptation of parameters in Bayesian networks. The performance of the Hugin Tool is illustrated using real-world Bayesian networks, commonly used examples from the literature, and randomly generated Bayesian networks.
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Madsen, A.L., Lang, M., Kjærulff, U.B., Jensen, F. (2003). The Hugin Tool for Learning Bayesian Networks. In: Nielsen, T.D., Zhang, N.L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2003. Lecture Notes in Computer Science(), vol 2711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45062-7_49
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DOI: https://doi.org/10.1007/978-3-540-45062-7_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40494-1
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