Implementation of Continuous Bayesian Networks Using Sums of Weighted
  Gaussians
  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.
