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I'm working on a project with datasets where the number of variables is 3000+ for which a bayesian network would be ideal choice to model the data. Unfortunately, as we all know, structure learning for Bayes Nets is exponential and consequently, unfeasible for such a wide dataset.
In my research, I've come across this paper which looks very encouraging as a method for addressing the exponential complexity of structure learning.
Is there a straightforward way to implement the parent constraint method described in the paper using the existing structure learning framework in the package?
Any insights or direction is much appreciated
Thanks!
Andrew
The text was updated successfully, but these errors were encountered:
Hi,
Great package. I really enjoy using it!
I'm working on a project with datasets where the number of variables is 3000+ for which a bayesian network would be ideal choice to model the data. Unfortunately, as we all know, structure learning for Bayes Nets is exponential and consequently, unfeasible for such a wide dataset.
In my research, I've come across this paper which looks very encouraging as a method for addressing the exponential complexity of structure learning.
Is there a straightforward way to implement the parent constraint method described in the paper using the existing structure learning framework in the package?
Any insights or direction is much appreciated
Thanks!
Andrew
The text was updated successfully, but these errors were encountered: