Rich, One idea is to use the EM algorithm and exploit the explicit representation of the noisy-OR (hidden inhibitors and deterministic OR nodes). My colleagues and me had a paper where we tried this idea (not only for the noisy-OR):
Zagorecki, A., Voortman, M. and Druzdzel, M., 2006. Decomposing Local Probability Distributions in Bayesian Networks for Improved Inference and Parameter Learning. The 19th International Florida Artificial Intelligence Research Symposium Conference. Best regards, Adam On Fri, Feb 11, 2011 at 11:32 AM, Rich Neapolitan <re-neapoli...@neiu.edu> 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 > > _______________________________________________ > uai mailing list > uai@ENGR.ORST.EDU > https://secure.engr.oregonstate.edu/mailman/listinfo/uai > > _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
