Hi,
I am a big fan of scikit-learn. I have been using it for two years now in
my Ph.D. and later in industry, and I wanted to say thank the community
that makes it possible.
I recently started to play with PyMC, which is great. However, I noticed
what seems to be a gap in machine learning methods for Python.
I have been unable to find any variational methods for inference in Python.
For example, belief propagation, loopy belief propagation, TRW, and the
many, many variants of these methods.
Are there any plans in Scikit-Learn to include support for some of these
algorithms? If not, does anyone have any pointers on where I can find
Python-friendly packages for this type of inference?
Generally speaking, assuming that you have a e.g. Bayesian network, and you
want to compute the posterior over some variables using variational
methods, how would you proceed with the existing landscape in Python?
Thanks a a lot,
Josh
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