Learning a L1-regularized Gaussian Bayesian network in the space of equivalence classes
نویسندگان
چکیده
Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper we focus on Gaussian Bayesian networks (GBN), that is, on continuous data and directed graphs. We propose to work in an equivalence class search space that, combined with regularization techniques to guide the search of the structure, allows to learn a sparse network close to the one that generated the data.
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