Okay, good to know. That’s also clarified a misunderstanding I’d had earlier. I’d thought KLqp had difficulty with both inverse gamma priors and variational models, but it seems it’s just the latter.
Also good to know that specifying variational models with the correct support prevents the need to transform the RVs. One gripe I have with PyMC is that their quickstart example is basically entirely about their variational inference implementation failing on a gaussian mixture. That’s good to know, but it is something which the user is powerless to fix, since PyMC ADVI is constrained to use a gaussian variational model!