Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians
Abstract
Bayesian networks provide a method of representing conditional independence between random variables and computing the probability distributions associated with these random variables. In this paper, we extend Bayesian network structures to compute probability density functions for continuous random variables. We make this extension by approximating prior and conditional densities using sums of weighted Gaussian distributions and then finding the propagation rules for updating the densities in terms of these weights. We present a simple example that illustrates the Bayesian network for continuous variables; this example shows the effect of the network structure and approximation errors on the computation of densities for variables in the network.
Cite
Text
Driver and Morrell. "Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians." Conference on Uncertainty in Artificial Intelligence, 1995.Markdown
[Driver and Morrell. "Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians." Conference on Uncertainty in Artificial Intelligence, 1995.](https://mlanthology.org/uai/1995/driver1995uai-implementation/)BibTeX
@inproceedings{driver1995uai-implementation,
title = {{Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians}},
author = {Driver, Eric and Morrell, Darryl},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1995},
pages = {134-140},
url = {https://mlanthology.org/uai/1995/driver1995uai-implementation/}
}