I recently read here in datamethods this discussion on prior distributions:
I’m impressed with the range of priors that have been proposed for use without reference to specific scientific problems, from cauchy(0, 2.5) to normal(0, 1/4). I find it a considerable range of skepticism!
At what point does it not suffice to say “with weakly informative priors”?
My question is more about transparent reporting of research methods than how to analyze data. On the one hand, more disclosure ought to be better. On the other hand, at some point more information becomes too much information. In example, I never read in papers using frequentist GLM that LM estimates were found with IWLS optimization.
Thanks in advance!