I am finding that while exp_quad kernel GP models require careful prior specification (see e.g. @betanalpha’s GP tutorial) of the length scale \rho, my exponential kernel GP models seem to fit fine with very basic constraints. For example, simply declaring
real<lower=L, upper=U> rho;
where L and U are the smallest and largest pairwise distance between observation locations works for me (meaning I recover parameters in fake data simulation and inference). Assuming that the locations were chosen well to capture the phenomenon of interest, this is a very weak prior.
I am happy to share some code but just wanted to check whether this conforms to anyone else’s experience and, if it is true, whether someone has an intuition as to why?