Highly Efficient Structural Learning of Sparse Staged Trees

Manuele Leonelli, Gherardo Varando
Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:193-204, 2022.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v186-leonelli22a, title = {Highly Efficient Structural Learning of Sparse Staged Trees}, author = {Leonelli, Manuele and Varando, Gherardo}, booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models}, pages = {193--204}, year = {2022}, editor = {Salmerón, Antonio and Rumı́, Rafael}, volume = {186}, series = {Proceedings of Machine Learning Research}, month = {05--07 Oct}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v186/leonelli22a/leonelli22a.pdf}, url = {https://proceedings.mlr.press/v186/leonelli22a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Highly Efficient Structural Learning of Sparse Staged Trees %A Manuele Leonelli %A Gherardo Varando %B Proceedings of The 11th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2022 %E Antonio Salmerón %E Rafael Rumı́ %F pmlr-v186-leonelli22a %I PMLR %P 193--204 %U https://proceedings.mlr.press/v186/leonelli22a.html %V 186 %X 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.
APA
Leonelli, M. & Varando, G.. (2022). Highly Efficient Structural Learning of Sparse Staged Trees. Proceedings of The 11th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 186:193-204 Available from https://proceedings.mlr.press/v186/leonelli22a.html.

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