I’m pretty new to Bayesian networks, but I was able to figure out a few things in Weka with the gui and the BayesNet algorithm. I’m trying to do the same thing programmatically in Python, and I wasn’t able to with the Weka wrapper. What I’d like to do is learn categories from a term document matrix with about 1300 columns(each is a word). After I have my model learned, I’d like to be able to do two things… 1.) to set evidence/observation to a category and see the probabilities per word change, and 2.) also send in a 1300 column vector and get a prediction as to the category it belongs to. Are these things possible using PyMC? Is there a tutorial/documentation doing something similar? I’ve looked and haven’t found anything yet. Thank you very much!