Rich,
adding to what has been said already: parameter learning for the
noisy-or model is also implemented
in the Primula implementation of Relational Bayesian Networks
( http://www.cs.aau.dk/~jaeger/Primula/index.html ).
This implementation uses a gradient-ascent approach (see the ICML-07 paper
http://www.cs.aau.dk/~jaeger/publications/ICML07.pdf), and can also be
used to learn parameters in
more complex models, e.g. nestings or mixtures of several noisy-or or
other combination functions.
Cheers,
Manfred
============================================================
Manfred Jaeger
Aalborg University
Dept. of Computer Science Phone: +45 9940 9856
Selma Lagerlöfs Vej 300 email: jae...@cs.aau.dk
9220 Aalborg www: http://www.cs.aau.dk/~jaeger
Denmark
Rich Neapolitan wrote:
Once again, I am going against the grain and submitting a post that is
not a job ad or a conference announcement. I hope no one takes offense.
My question concerns the noisy-OR model. The traditional way to assess
a parameter value for a given cause is to use the data items that only
have that cause present. However, if there are many causes and limited
data, there will be few such data items. I want an approximation
method that deals with this problem. A quick Google search did not
reveal any previous work in this area. I have a few ideas, but I
thought I would first ask if anyone knows of anything that has already
been done in this area.
Best regards,
Rich
Rich Neapolitan
Professor and Chair of Computer Science
Northeastern Illinois University
5500 N. St. Louis
Chicago, Il 60625
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