It would be great if you could help testing it out. Right now there is only one example available for PyStan on the website but trying it out on real models would be amazing.

I have recently refocused on this a little with hopes to expanding to PSIS-LFO and SMC, and I started working on the PyMC3 wrapper too on this PR. It would be great if you could checkout the branch and try it. I’ll rebase it with master.

Getting it to work with PyMC3 has its issues, but it can be sorted out. The PR actually has 2 proposals that try to abstract the process as much as possible, feedback on the general API and on how these 2 alternatives compare will be most appreciated. I still want to try a 3rd option using symbolic_pymc though, I hope I’ll have time within the following 2 weeks.

I personally prefer the alternative that relies more on xarray instead of the one that uses pymc3 more intensively as I think it requires less mental overhead to use. Here is a link to the notebook which I think you’ll be able to use modifying only the `sel_observations`

method.

The link to the second alternative is here. Unlike in the previous case, here there is no need to rewrite with numpy the pointwise log likelihood formula, but the resulting class may be slightly harder to generalize.

Let me know if you need help adapting this to your models