Effective and efficient structure learning with pruning and model averaging strategies

DOI10.1016/j.ijar.2022.09.016OpenAlexW4226399656MaRDI QIDQ2105581

Yang Liu, Anthony C. Constantinou, Kiattikun Chobtham, Neville K. Kitson, Zhi-gao Guo

Publication date: 8 December 2022

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2112.00398

zbMATH Keywords

Bayesian networksnoisy dataprobabilistic graphical modelsdirected acyclic graphsMarkov equivalence

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (1)

Being Bayesian about learning Gaussian Bayesian networks from incomplete data

Uses Software

• bnlearn
• TETRAD

Cites Work

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• The max-min hill-climbing Bayesian network structure learning algorithm
• Causation, prediction, and search
• On scoring maximal ancestral graphs with the max-min hill climbing algorithm
• Ancestral graph Markov models.
• Learning Bayesian networks: The combination of knowledge and statistical data
• Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs
• 10.1162/153244302760200696

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