The algebraic eigenvalue problem
The algebraic eigenvalue problem
An introduction to variational methods for graphical models
Learning in graphical models
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The Information bottleneck EM algorithm
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
New d-separation identification results for learning continuous latent variable models
ICML '05 Proceedings of the 22nd international conference on Machine learning
Model-based multidimensional clustering of categorical data
Artificial Intelligence
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In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We present a general method for significantly speeding the structure search algorithm for continuous variable networks with common parametric distributions. Importantly, our method facilitates the addition of new hidden variables into the network structure efficiently. We demonstrate the method on several data sets, both for learning structure on fully observable data, and for introducing new hidden variables during structure search.