Independency relationships and learning algorithms for singly connected networks

DOI10.1080/095281398146743zbMath1049.68621OpenAlexW2143837989MaRDI QIDQ4421280

Luis M. de Campos

Publication date: 1998

Published in: Journal of Experimental & Theoretical Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/095281398146743

zbMATH Keywords

Bayesian networksMarkov networks

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (7)

Ant colony optimization for learning Bayesian networks. ⋮ The BNR model: Foundations and performance of a Bayesian network-based retrieval model. ⋮ High-dimensional consistency in score-based and hybrid structure learning ⋮ A hybrid methodology for learning belief networks: BENEDICT ⋮ Inference in directed evidential networks based on the transferable belief model ⋮ An iterated local search algorithm for learning Bayesian networks with restarts based on conditional independence tests ⋮ A new approach for learning belief networks using independence criteria

Cites Work

• Fusion, propagation, and structuring in belief networks
• Stochastic independence, causal independence, and shieldability
• Axioms and algorithms for inferences involving probabilistic independence
• Graphoids and natural conditional functions
• Influence diagrams for statistical modelling
• Probabilistic Inference and Influence Diagrams
• Independence properties of directed markov fields
• Approximating discrete probability distributions with dependence trees
• On Information and Sufficiency
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