Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Planning and control
An entropy-based learning algorithm of Bayesian conditional trees
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Connectionist learning of belief networks
Artificial Intelligence
Real-world applications of Bayesian networks
Communications of the ACM
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Bayesian networks for knowledge discovery
Advances in knowledge discovery and data mining
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Predicting protein folds with structural repeats using a chain graph model
ICML '05 Proceedings of the 22nd international conference on Machine learning
Sensor-based understanding of daily life via large-scale use of common sense
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Scalable statistical learning: a modular Bayesian/Markov network approach
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A graphical characterization of the largest chain graphs
International Journal of Approximate Reasoning
IPF for discrete chain factor graphs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
An alternative Markov property for chain graphs
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A graph-theoretic analysis of information value
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
On separation criterion and recovery algorithm for chain graphs
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning bayesian networks structure with continuous variables
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Towards an integrated protein-protein interaction network
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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Chain graphs combine directed and undirected graphs and their underlying mathematics combines properties of the two. This paper gives a simplified definition of chain graphs based on a hierarchical combination of Bayesian (directed) and Markov (undirected) networks. Examples of a chain graph are multivariate feed-forward networks, clustering with conditional interaction between variables, and forms of Bayes classifiers. Chain graphs are then extended using the notation of plates so that samples and data analysis problems can be represented in a graphical model as well. Implications for learning are discussed in the conclusion.