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Several structural learning algorithms for staged tree models, an asymmetricextension of Bayesian networks, have been defined. However, they do not scaleefficiently as the number of variables considered increases. Here we introducethe first scalable structural learning algorithm for staged trees, whichsearches over a space of models where only a small number of dependencies canbe imposed. A simulation study as well as a real-world application illustrateour routines and the practical use of such data-learned staged trees.

Highly Efficient Structural Learning of Sparse Staged Trees