Hello all, I’m trying to make use of Gaussian Processes for model calibration in Stan. Something that is often recommended is to re-scale your predictors in the interval [0, 1] which improves sampling efficiency [1,2]. By reading stan’s user guide on standardisation I can see how to do that with my predictors using the stan syntax. From the same page, it is suggested that the priors could have been transformed as well.

What I’m wondering is how would one go about transforming them? I understand why the priors were not transformed in that example, since they were diffuse. In my case, some of the priors relate to the probability of occurrence of the predictors. If the priors are normal or uniform, I found it easy to change the parameters that define them to re-scale appropriately but not for other distributions such as Weibull. If you have informative priors, could you transform them in a similar way as you transform the data but within the transformed parameter block? (i.e. sample tf ~ N(24,4) in the model block and then in the transformed parameter block apply a linear transformation to re-scale tf: tf_std = (tf - tf_min)/(tf_max - tf_min) where tf_min and tf_max come from my computer model data)?

References to what I’m trying to reproduce:

[1] https://epubs.siam.org/doi/abs/10.1137/S1064827503426693

[2] https://www.sciencedirect.com/science/article/pii/S0378778818307539