There has always been a temptation I’ve felt, to, after the first version of my model has run, update all my variables’ priors to match the results of the `pm.traceplot()`

graphs. Is this a valid method of improving one’s model?

I had read a quote a while back from Gelman that trace plots can indeed be utilized to further inform one’s priors, but there has to be some limit to this. *Otherwise*, why wouldn’t the default modelling process always be to

- run the model with vague priors, and
- if the traceplots create “stable enough” looking variable distributions, use kernel methods on each stable variable to gain its mean and std, and feed those into the respective model variables?

I feel this is an amateurish question, but would someone please elucidate me on how to properly use traceplots (assuming everything has already converged correctly so there are no problems there) to better one’s model?