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Conference Paper

Distribution-free Learning of Bayesian Network Structure

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84243

Sun,  X
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Sun, X. (2008). Distribution-free Learning of Bayesian Network Structure. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, 423-439.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C73B-7
Abstract
We present an independence-based method for learning Bayesian network (BN) structure without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous domains. Even mixed continuous-categorical domains and structures containing vectorial variables can be handled. We address the problem by developing a non-parametric conditional independence test based on the so-called kernel dependence measure, which can be readily used by any existing independence-based BN structure learning algorithm. We demonstrate the structure learning of graphical models in continuous and mixed domains from real-world data without distributional assumptions. We also experimentally show that our test is a good alternative, in particular in case of small sample sizes, compared to existing tests, which can only be used in purely categorical or continuous domains.