Paper
- F. J. Díez. Parameter adjustment in BN´s.
The generalized noisy OR-gate. Proc. 9th Conference on
Uncertainty in AI, Washington, DC, July 1993,
pp. 99-105.
- 7 pages. PostScript
(170 KB), zip
version (52 KB), BibTeX
entry.
Abstract
Spiegelhalter and Lauritzen (1990) 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 lambda and pi 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.
Javier Díez
/ Last update: February 22 1999.