Poor formatting, but this will sort of get you what you want:
for group in ["prior", "prior_predictive", "observed_data"]:
print(f"{group}")
for var in list(model_prior[group].keys()):
print(f" {var}: {model_prior[group][var].shape}")
But in general, you would probably not want to do this because there could be a large number of variables in each group. On top of that, you are using shapes rather than the preferred coords/dims that you see when you inspect an individual group (see here and here for more info on using dimensions).
In particular, this tells you that p has 2 dimensions (chain and draw) and that chain has 1 coordinate and draw has 100. But the fact that you have 2 dimensions and (now) know what those dimensions are typically provides you with much more information than something like p: (1, 100).
Hi Christian, I’ll take a closer look at the two documents you sent me, and I think I’ll find what I’m looking for there.
For the moment, I do not yet know how to properly handle these coords and dims; so, these docs will certainly be of great help for what I had in mind: to present some results in a concentrate and concise way, for example in the idea of projecting slides for a powerpoint presentation.
I’d highly recommend spending some time going over one (or more) of the example notebooks. They provide examples of best practice. I’d recommend taking a look at one of the GLM examples. Though not all them require coords, virtually all of them show examples of using the information in the returned inferencedata.