Rich,
Judea is right. In fact, Radford Neal discussed both logistic
regression and noisy-OR belief nets (with hidden variables) almost 20
years ago, and used stochastic gradient descent to fit them (this was
long before deep belief nets became fashionable :)
If you have more positive examples than negative at a given node, you
could use a prior
to regularize the problem.
HTH
Kevin
@article{Neal92,
author = "R. Neal",
title = "Connectionist learning of belief networks",
journal = "Artificial Intelligence",
volume =56,
pages = "71--113",
year = 1992,
url =
"http://www.cs.toronto.edu/~radford/belief-net.abstract.html#ref-belief-net";
}
On Sat, Feb 12, 2011 at 4:57 PM, Judea Pearl <ju...@cs.ucla.edu> wrote:
> Rich,
> Why would it be different from logistic regression, for which there is
> a volume of statistical literature.?. (If I take logP(x=0|pa(x)), I get a
> linear
> expression in the parameters, the rest should fall in place)
>
> ==Judea
>
> ----- Original Message -----
> From: Rich Neapolitan
> To: uai@engr.orst.edu
> Sent: Friday, February 11, 2011 8:32 AM
> Subject: [UAI] Learning Parameters for the Noisy-OR model
> 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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