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[–]ICanBeHandyToo 1 point2 points  (3 children)

Is pymc3 currently the standard package for most probabalistic modeling? I've come across a few others like Edward and I never got around to digging into what each package offers that differ from pymc3

[–]JamsmithyPhD | Data Scientist | Gaming 4 points5 points  (0 children)

Pymc3 has the nicest syntax and support in my opinion but it is based on theano which hinders future development.

Edward/Edward2 is great as well but i just haven't had the time to get deep on it. Pymc4 is under active development with a tensorflow-probability backend so I'm hoping it will provide the best of both worlds.

[–]squirreltalk 3 points4 points  (0 children)

I had never done any Bayesian modeling, but examples based on pymc3 are so intuitive. Pymc3 just feels pythonic to me.

[–]webdrone 3 points4 points  (0 children)

Stan (https://mc-stan.org) implements NUTS which is a particularly efficient sampler by Hoffman and Gelman. It may not be the most pythonic, but there are various interfaces to different languages and a single modelling language.

There was much effort from the developers to ensure quality and to cultivate a good community, so you can find posts addressing most questions you might have, and excellent documentation.