Marginal information for structure learning

DOI10.1007/s11222-019-09877-xzbMath1436.62210OpenAlexW2956892928WikidataQ127471281 ScholiaQ127471281MaRDI QIDQ2302495

Gang-Hoo Kim, Sung-Ho Kim

Publication date: 26 February 2020

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-019-09877-x

zbMATH Keywords

EM algorithmmarginal modelBayesian networkbootstrappingscore functionedge-error probabilityexperts' knowledge

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Learning and adaptive systems in artificial intelligence (68T05) Contingency tables (62H17)

Uses Software

• bnlearn
• Hailfinder

Cites Work

• Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood
• The max-min hill-climbing Bayesian network structure learning algorithm
• Conditional log-linear structures for log-linear modelling
• Markov fields and log-linear interaction models for contingency tables
• A Bayesian method for the induction of probabilistic networks from data
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Adaptive probabilistic networks with hidden variables
• Ancestral graph Markov models.
• Marginalizing and conditioning in graphical models
• Learning Bayesian networks: The combination of knowledge and statistical data
• Combining Conditional Log-Linear Structures
• 10.1162/153244303321897717
• On Information and Sufficiency
• The $\chi^2$ Test of Goodness of Fit
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