Neat. I found out that I can also use describe(p_est), leading to two element vector where summarize(p_est) = describe(p_est)[1], while element 2 contains the quantiles.

One more thingâ€¦ (as they say):

does p_est contain the prior distributions that were defined in the model, or would I need to take them out of the Turing model? [Iâ€™d like to compare the densities of the priors and the posteriorsâ€¦]

Assuming p_est is a Chains object from a call to sample, it just contains posterior information. You can call sample in a way to get samples from the prior, have a look at the docs. Then you could compare say marginal densities using a KDE