[crispycrafter2]

I wonder if this was borne out of the success of NumPyro. Been playing around with, pystan and NumPyro recently. Mostly because pymc3 is going the way of the dodo.

NumPyro / JAX / PyTorch just seems like the most versatile offering out there right now

JAX

[btwillard]

I'm the author of the JAX backend in Theano, and, no, this didn't have anything to do with NumPyro--especially since I neither use nor know very much about NumPyro.

It was just a quick demonstration of how easily one can use Theano as a generalized graph "front-end", while also preserving its more unique and programmable symbolic optimization capabilities. JAX was one of a few "backends" I considered, and, due to the JAX Python library, it also looked like the most straightforward one to implement first.

[mushufasa]

> pymc3 is going the way of the dodo

on the contrary, i recently was looking for python libraries for some bayesian computations for some greenfield development and pymc3 was at the top of the list. with statistical libraries, i prioritize well-tested, large community, and extensive documentation. if others share the same priorities, there's a long future for pymc3.

[crispycrafter2]

That's good to know. My comment was borne out of the uncertainty around pymc4 with tensorflow. We are currently using Stan in production and have been hesitant to pull the trigger on pymc3/4 due to this uncertainty.

I welcome and applaud the choice of JAX as it shows a lot of promise with autograd and flexible execution targets.