We propose a new method for learning the parameters of a Bayesian network with qualitative influences. The proposed method aims to remove unwanted (context-specific) independencies that are created by the order-constrained maximum likelihood (OCML) estimator. This is achieved by averaging the OCML estimator with the fitted probabilities of a first-order logistic regression model. We show experimentally that the new learning algorithm does not perform worse than OCML, and resolves a large part of the independencies. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Feelders, A., & Van Straalen, R. (2007). Parameter learning for Bayesian networks with strict qualitative influences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4723 LNCS, pp. 48–58). Springer Verlag. https://doi.org/10.1007/978-3-540-74825-0_5
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