Learning Bayesian Networks by Branching on Constraints
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Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:511522, 2018.
Abstract
We consider the Bayesian network structure learning problem, and present a new algorithm for enumerating the $k$ best Markov equivalence classes. This algorithm is scorebased, but uses conditional independence constraints as a way to describe the search space of equivalence classes. The techniques we use here can potentially lead to the development of scorebased methods that deal with more complex domains, such as the presence of latent confounders or feedback loops. We evaluate our algorithm’s performance on simulated continuous data.
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