As always, we're happy to accept to contributions! If you're looking to get involved, now is a great time. Please don't hesitate to speak up or reach out, either on the Theano-PyMC GitHub repo (https://github.com/pymc-devs/Theano-PyMC) or some other way (my website's in my bio).
In that regard, I am curious about why Tensorflow didn't work out. I understand Tensorflow version 1 implements a declarative mode that I guess is in many ways similar to Theano's. I'm assuming v2 still supports that mode, on top of the new eager mode -- is that the case? If so, was there some aspect of its implementation that made it unsuitable for PyMC?
In order to improve the performance and usability of PyMC, I believe we need to automate things at the graph level, and Theano is by far the most suitable for this--between the two, at least.
You can find some work along these lines in the Symbolic PyMC project (https://github.com/pymc-devs/symbolic-pymc); it contains symbolic work done in both Theano and TensorFlow.
Will the JAX backend and integration with external JAX modules mean that we’ll see improvements to PyMC3’s variational inference module? That would really increase the versatility of PyMC3 for probabilistic modelling in Python.