Learning an L1-regularized Gaussian Bayesian network in the equivalence class space.
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, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We prop...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2010
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