I’ve updated my model to use a truncated normal for the likelihood.

The model samples the posterior in the same time, but both prior & posterior predictive takes much longer (20s to 15min for posterior predictive).

How is that? I thought that truncating would increase speeds.

In my opinion, truncation would increase uncertainty (CrI are wider). This may increase the calculation times

Generating random draws from the Truncated Normal is probably slower than fr9m a vanilla Normal.

I think we are just using scipy `trunc_norm`

. You can see if taking rvs eith similar parameters as in your model is indeed so mich slower.

Can you share a small reproducible snippet that illustrates the slowdown?