A new approach for learning belief networks using independence criteria

DOI10.1016/S0888-613X(99)00042-0zbMath0995.68109OpenAlexW2133476056WikidataQ127771352 ScholiaQ127771352MaRDI QIDQ1605675

Juan F. Huete, Luis M. de Campos

Publication date: 23 July 2002

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

Full work available at URL: https://doi.org/10.1016/s0888-613x(99)00042-0

zbMATH Keywords

belief networks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Knowledge representation (68T30) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (14)

Hypergraphs as a mean of discovering the dependence structure of a discrete multivariate probability distribution ⋮ Ant colony optimization for learning Bayesian networks. ⋮ Structural learning of Bayesian networks by bacterial foraging optimization ⋮ Score-based methods for learning Markov boundaries by searching in constrained spaces ⋮ Polyhedral approaches to learning Bayesian networks ⋮ BNC-PSO: structure learning of Bayesian networks by particle swarm optimization ⋮ Learning Bayesian networks for discrete data ⋮ A hybrid methodology for learning belief networks: BENEDICT ⋮ Estimating probability values from an incomplete dataset ⋮ Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs ⋮ An iterated local search algorithm for learning Bayesian networks with restarts based on conditional independence tests ⋮ Bayesian network learning algorithms using structural restrictions ⋮ Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions ⋮ Local permutation tests for conditional independence

Uses Software

• TETRAD
• BENEDICT

Cites Work

• Decision theoretic generalizations of the PAC model for neural net and other learning applications
• Causation, prediction, and search
• A Bayesian method for the induction of probabilistic networks from data
• Construction of Bayesian network structures from data: A brief survey and an efficient algorithm
• Bayesian analysis in expert systems. With comments and a rejoinder by the authors
• Learning Bayesian networks: The combination of knowledge and statistical data
• Independency relationships and learning algorithms for singly connected networks
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