Discovering causes and effects of a given node in Bayesian networks

DOI10.1007/s11464-013-0285-yzbMath1273.62060OpenAlexW2078241172MaRDI QIDQ372237

Changzhang Wang, Zhi Geng, You Zhou

Publication date: 14 October 2013

Published in: Frontiers of Mathematics in China (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11464-013-0285-y

zbMATH Keywords

graphical modelsdirected acyclic graphsstructure learningcausal networks

Mathematics Subject Classification ID

Bayesian inference (62F15) Applications of graph theory (05C90) Sequential statistical methods (62L99) Sequential statistical analysis (62L10) Directed graphs (digraphs), tournaments (05C20)

Uses Software

• TETRAD
• Hailfinder

Cites Work

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• The max-min hill-climbing Bayesian network structure learning algorithm
• A note on minimal d-separation trees for structural learning
• Causation, prediction, and search
• Adaptive probabilistic networks with hidden variables
• Learning Bayesian networks: The combination of knowledge and statistical data
• Decomposition of structural learning about directed acyclic graphs
• Tabu Search-Enhanced Graphical Models for Classification in High Dimensions

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