I confess that I have never understood why Bayesian statisticians would ever report just a single set of "results". One of the key insights of the Reverend Thomas Bayes was that the data gives you a map between what you thought before and what you should think now. Thus I would think that the Bayesian tradition would be to report this map—not just what the posterior mean is for one set of prior beliefs, but other things as well, like how different your prior beliefs would have to have been in order to support a posterior mean that hits some target for economic significance, and so forth. But they do not:

Andrew Gelman: Here’s an Idea for Not Getting Tripped Up with Default Priors: "Here’s an idea for not getting tripped up with default priors: For each parameter (or other qoi), compare the posterior sd to the prior sd. If the posterior sd for any parameter (or qoi) is more than 0.1 times the prior sd, then print out a note: “The prior distribution for this parameter is informative.” Then the user can go back and check that the default prior makes sense for this particular example...


#noted #2019-12-04

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