Hi Shankar. I am not really saying that graphical models are not useful tools. What I am questioning is whether it is reasonable to formulate these problems as general as you do and how they fit into the scikit-learn.
By the way, there are several "Bayes nets" already implemented in the scikit-learn, for example logistic regression and Bayesian Regression and Gaussian mixture models (which have latent variables). Let's stay in case 1), where the model and parameters are know, which is the easiest case. Can you describe how you would want to implement this? If you can make a reasonable proposal that stays within the scope of scikit-learn, I would gladly mentor your project. Btw, there is also PyMC which implements a language to describe models and do Monte Carlo inference. Cheers, Andy ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
