Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate
Francisco Diez
Abstract:
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.
Keywords:
Pages: 99-105
PS Link:
PDF Link: /papers/93/p99-diez.pdf
BibTex:
@INPROCEEDINGS{Diez93,
AUTHOR = "Francisco Diez ",
TITLE = "Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "99--105"
}


hosted by DSL   •   site info   •   help