Highly Efficient Structural Learning of Sparse Staged Trees
This work addresses a scalability problem for researchers and practitioners using staged tree models, but it is incremental as it builds on existing methods with a focus on efficiency.
The paper tackles the scalability issue in structural learning algorithms for staged tree models by introducing the first scalable algorithm that searches over a space with limited dependencies, demonstrating its utility through a simulation study and real-world application.
Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number of variables considered increases. Here we introduce the first scalable structural learning algorithm for staged trees, which searches over a space of models where only a small number of dependencies can be imposed. A simulation study as well as a real-world application illustrate our routines and the practical use of such data-learned staged trees.