Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate
  Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian
networks and proposed three models for the representation of conditional
probabilities. A forth model, shown here, assumes that the parameter
distribution is given by a product of Gaussian functions and updates them from
the _ and _r messages of evidence propagation. We also generalize the noisy
OR-gate for multivalued variables, develop the algorithm to compute probability
in time proportional to the number of parents (even in networks with loops) and
apply the learning model to this gate.
