Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Machine Learning - Special issue on learning with probabilistic representations
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Learning hybrid Bayesian networks from data
Learning in graphical models
Bayesian networks for lossless dataset compression
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Very fast EM-based mixture model clustering using multiresolution kd-trees
Proceedings of the 1998 conference on Advances in neural information processing systems II
Efficient Locally Weighted Polynomial Regression Predictions
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A multivariate discretization method for learning Bayesian networks from mixed data
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Implementation of continuous Bayesian networks using sums of weighted Gaussians
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Models and selection criteria for regression and classification
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Short communication: On estimating simple probabilistic discriminative models with subclasses
Expert Systems with Applications: An International Journal
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Recently developed techniques have made it possible to quickly learn accurate probability density functions from data in low-dimensional continuous spaces. In particular, mixtures of Gaussians can be fitted to data very quickly using an accelerated EM algorithm that employs multiresolution kd-trees (Moore, 1999). In this paper, we propose a kind of Bayesian network in which low-dimensional mixtures of Gaussians over different subsets of the domain's variables are combined into a coherent joint probability model over the entire domain. The network is also capable of modeling complex dependencies between discrete variables and continuous variables without requiring discretization of the continuous variables. We present efficient heuristic algorithms for automatically learning these networks from data, and perform comparative experiments illustrating how well these networks model real scientific data and synthetic data. We also briefly discuss some possible improvements to the networks, as well as possible applications.