A Fast Hill-Climbing Algorithm for Bayesian Networks Structure Learning

DOI10.1007/978-3-540-75256-1_52zbMath1148.68514OpenAlexW1575390667MaRDI QIDQ3524969

Juan L. Mateo, José A. Gámez, José M. Puerta

Publication date: 16 September 2008

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-75256-1_52

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (3)

Score-based methods for learning Markov boundaries by searching in constrained spaces ⋮ Inferring gene regulatory networks by an order independent algorithm using incomplete data sets ⋮ Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method

Uses Software

• TETRAD
• BENEDICT

Cites Work

• The max-min hill-climbing Bayesian network structure learning algorithm
• Causation, prediction, and search
• Bayesian network classifiers
• Ant colony optimization for learning Bayesian networks.
• Learning Bayesian networks: The combination of knowledge and statistical data
• 10.1162/153244303321897717
• Symbolic and Quantitative Approaches to Reasoning with Uncertainty
• A hybrid methodology for learning belief networks: BENEDICT
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