Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Learning hybrid Bayesian networks from data
Learning in graphical models
Machine Learning
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Fast factored density estimation and compression with bayesian networks
Fast factored density estimation and compression with bayesian networks
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
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
Learning Bayesian networks: a unification for discrete and Gaussian domains
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Conditional Density Estimation with Class Probability Estimators
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
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Joint distributions over many variables are frequently modeled by decomposing them into products of simpler, lower-dimensional conditional distributions, such as in sparsely connected Bayesian networks. However, automatically learning such models can be very computationally expensive when there are many datapoints and many continuous variables with complex nonlinear relationships, particularly when no good ways of decomposing the joint distribution are known a priori. In such situations, previous research has generally focused on the use of discretization techniques in which each continuous variable has a single discretization that is used throughout the entire network. In this paper, we present and compare a wide variety of tree-based algorithms for learning and evaluating conditional density estimates over continuous variables. These trees can be thought of as discretizations that vary according to the particular interactions being modeled; however, the density within a given leaf of the tree need not be assumed constant, and we show that such nonuniform leaf densities lead to more accurate density estimation. We have developed Bayesian network structure-learning algorithms that employ these tree-based conditional density representations. and we show that they can be used to practically learn complex joint prob ability models over dozens of continuous variables from thousands of datapoints. We focus on finding models that are simultaneously accurate, fast to learn, and fast to evaluate once they are learned.