Computer Science Department
School of Computer Science, Carnegie Mellon University


Mixed-nets: Factored Mixtures of Gaussians in Bayesian Networks
with Mixed Continuous and Discrete Variables

Scott Davies, Andrew Moore

March 2000

Keywords: Bayesian networks, mixture models, machine learning

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. 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 modelling 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 their possible application to anomaly detection, classification, probabilistic inference, and compression.

28 pages

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