Abstract
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.
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Feelders, A., van Straalen, R. (2007). Parameter Learning for Bayesian Networks with Strict Qualitative Influences. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_5
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DOI: https://doi.org/10.1007/978-3-540-74825-0_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74824-3
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