Parameter learning for Bayesian networks with strict qualitative influences

Authors:
Ad Feelders;Robert Van Straalen
Affiliations:
Utrecht University, Department of Information and Computing Sciences, Utrecht, The Netherlands;Utrecht University, Department of Information and Computing Sciences, Utrecht, The Netherlands
Venue:
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Year:
2007

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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.