Date of Original Version
4-2000
Type
Technical Report
Abstract or Table of Contents
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 fi tted to data very quickly using an accelerated EM algorithm that employs multi-resolution kdtrees (Moore, 1999). In this paper, we propose a kind of Bayesian network in which low-dimensional mixtures of Gaussians over di fferent 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 eÆcient heuristic algorithms for automatically learning these networks from data, and perform comparative experiments illustrating how well these networks model real scienti fic data and synthetic data. We also briefly discuss some possible improvements to the networks, as well as their possible application to anomaly detection, classifi cation, probabilistic inference, and compression.