The dual PC algorithm and the role of Gaussianity for structure learning of Bayesian networks

DOI10.1016/j.ijar.2023.108975arXiv2112.09036MaRDI QIDQ6137867

Giusi Moffa, Jack Kuipers, Enrico Giudice

Publication date: 4 September 2023

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

Full work available at URL: https://arxiv.org/abs/2112.09036

zbMATH Keywords

Bayesian networksgraphical modelsdirected acyclic graphsstructure learningcopula modelsPC algorithm

Mathematics Subject Classification ID

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

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The max-min hill-climbing Bayesian network structure learning algorithm
• Estimating high-dimensional intervention effects from observational data
• Causation, prediction, and search
• Characterizations of multivariate normality: I. Through independence of some statistics
• A characterization of Markov equivalence classes for acyclic digraphs
• Bayesian network based extreme learning machine for subjectivity detection
• High-dimensional semiparametric Gaussian copula graphical models
• A PC algorithm variation for ordinal variables
• Testing multivariate normality
• PARTIAL CORRELATION AND CONDITIONAL CORRELATION AS MEASURES OF CONDITIONAL INDEPENDENCE
• 10.1162/153244303321897717
• Efficient Sampling and Structure Learning of Bayesian Networks

This page was built for publication: The dual PC algorithm and the role of Gaussianity for structure learning of Bayesian networks