Mixnets: Learning Bayesian Networks with mixtures of discrete and continuous attributes (2000)
Tags
Astrostatistics, Bayesian Networks, Statistical Data Mining for Astrophysics
Abstract
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.
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Approximate BibTeX Entry
@inproceedings{davies-mixnets,
Year = {2000},
Publisher = {AAAI Press},
Booktitle = {Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence},
Author = {
Scott Davies, Andrew
Moore
},
Title = {Mixnets: Learning Bayesian Networks with mixtures of discrete and continuous attributes}
}