mlsanityData to 7 Oct 2026

Highly Efficient Structural Learning of Sparse Staged Trees

arXiv 2206.06970 ↗First posted 14 Jun 2022stat.ML cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 14 Jun 2022.

Builds on

11 of 29 references.

References · 11

  1. 2013Context-Specific Independence in Bayesian Networkscited by 43
  2. 2013Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithmcited by 22
  3. 2020The R Package stagedtrees for Structural Learning of Stratified Staged Treescited by 17
  4. 2013A Bayesian Approach to Learning Bayesian Networks with Local Structurecited by 15
  5. 2021Context-Specific Causal Discovery for Categorical Data Using Staged Treescited by 10
  6. 2013Learning Bayesian Networks with Local Structurecited by 9
  7. 2020The curved exponential family of a staged treecited by 9
  8. 2021Staged trees and asymmetry-labeled DAGscited by 8
  9. 2022Structural Learning of Simple Staged Treescited by 7
  10. 2020A new class of generative classifiers based on staged tree modelscited by 6
  11. 2020Algebraic geometry of discrete interventional modelscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 1.

6 papers.