Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Learning in graphical models
Independence in uncertainty theories and its applications to learning belief networks
Handbook of defeasible reasoning and uncertainty management systems
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Bayesian Networks
Bayesian Network Learning with Parameter Constraints
The Journal of Machine Learning Research
Articulatory feature recognition using dynamic Bayesian networks
Computer Speech and Language
Adaptive learning algorithms for Bayesian network classifiers
AI Communications
A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients
Expert Systems with Applications: An International Journal
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Learning a restricted Bayesian network for object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An algorithm for the construction of Bayesian network structures from data
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Bayesian network multi-classifiers for protein secondary structure prediction
Artificial Intelligence in Medicine
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Discovering gene association networks by multi-objective evolutionary quantitative association rules
Journal of Computer and System Sciences
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When it comes to learning graphical models from data, approaches based on conditional independence tests are among the most popular methods. Since Bayesian networks dominate research in this field, these methods usually refer to directed graphs, and thus have to determine not only the set of edges, but also their direction. At least for a certain kind of possibilistic graphical models, however, undirected graphs are a much more natural basis. Hence, in this area, algorithms for learning undirected graphs are desirable, especially, since first learning a directed graph and then transforming it into an undirected one wastes resources and computation time. In this paper I present a general algorithm for learning undirected graphical models, which is strongly inspired by the well-known Cheng-Bell-Liu algorithm for learning Bayesian networks from data. Its main advantage is that it needs fewer conditional independence tests, while it achieves results of comparable quality.