Structure learning of Bayesian networks by continuous particle swarm optimization algorithms

DOI10.1080/00949655.2018.1440395OpenAlexW2583046099WikidataQ115006608 ScholiaQ115006608MaRDI QIDQ4960626

Xu-Qing Liu, Xinsheng Liu

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1440395

zbMATH Keywords

Bayesian networkstructure learning(conditional) mutual information(continuous) particle swarm optimization

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• BNT

Cites Work

• The max-min hill-climbing Bayesian network structure learning algorithm
• Learning Bayesian networks from data by particle swarm optimization
• A Bayesian method for the induction of probabilistic networks from data
• Estimating the dimension of a model
• Learning Bayesian networks from data: An information-theory based approach
• Learning Bayesian networks: The combination of knowledge and statistical data
• An optimization-based approach for the design of Bayesian networks
• On the incompatibility of faithfulness and monotone DAG faithfulness
• Improved model selection criterion
• Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection
• Research Progress of Probabilistic Graphical Models: A Survey
• A simulated annealing-based method for learning Bayesian networks from statistical data
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