On Thu, Mar 14, 2019 at 10:11:41PM +0530, Subham Barnwal wrote: > I would love to build some "Bayesian Methods Based Libraries" which i guess > except TensorFlow probability people don't have too much options.Some of > the things I am planning is to implement BlackBox VI,GP with > scalability,EM(I see GMM is already there but may be I can try coding a > more general purpose EM) etc.I am open to any other suggestions (Both > bayesian and non-bayesian).I would appreciate your guidance on the > same.Please help me over the same.
Hi Subham, Thanks for getting in touch and the ideas that you've proposed look interesting to me. If you prepare a proposal for these things, probably one of the most important things to focus on is the API that is provided to users for these techniques. Specifically for a general-purpose EM implementation, it would be great if we could use that in place of the existing EM implementation used for GMMs. You can take a look at the `EMFit<>` class in src/mlpack/methods/gmm/em_fit.hpp to see how it's currently structured. Code reuse is a big priority for mlpack, so it would be good to take a look through the library and see what components you could reuse or adapt for your implementations. :) Hope this is helpful! Let me know if I can clarify anything. Thanks, Ryan -- Ryan Curtin | "So, it's just you 57 punks against KUNG FU JOE?" [email protected] | - Kung Fu Joe _______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
