Uncertainty in Artificial Intelligence
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Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians
Eric Driver, Darryl Morrell
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.
Keywords: Bayesian Networks, continuous variables, sums of Gaussian approximations.
Pages: 134-140
PS Link: FTP://trcsun3.eas.asu.edu/pub/UAI95/Continuous_Networks.ps
PDF Link: /papers/95/p134-driver.pdf
BibTex:
@INPROCEEDINGS{Driver95,
AUTHOR = "Eric Driver and Darryl Morrell",
TITLE = "Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "134--140"
}


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