Statistical models are placeholders. We lay down a model, fit it to data, use the fitted model to make inferences about quantities of interest (qois), check to see if the model’s implications are consistent with data and substantive information, and then go back to the model and alter, fix, update, augment, etc.

Given that models are placeholders, we’re interested in the dependence of inferences on model assumptions. In particular, with Bayesian inference we’re often concerned about the prior.

With that in mind, awhile ago I came up with this recommendation.

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.”

The idea here is that if the prior distribution is informative in this way, it can make sense to think harder about it, rather than just accepting the placeholder.

I’ve been interested in using this idea and formalizing it, and then the other day I got an email from Virginia Gori, who wrote:

I recently read your contribution to the Stan wiki page on priors choice recommendations, suggesting to ensure that the ratio of the standard deviations of the posterior and the prior(s) is at least 0.1 to assesss how informative priors are.

I found it very useful, and would like to use it in a publication. Searching online, I could only find this criteria in the Stan manual. I wonder if there’s a peer reviewed publication on this I should reference.

I have no peer-reviewed publication, or even any clear justification of the idea, nor have I seen it in the literature. But it could be there.

So this post serves several functions:

– It’s something that Gori can point to as a reference, if the wiki isn’t enough.

– It’s a call for people (You! Blog readers and commenters!) to point us to any relevant literature, including ideally some already-written paper by somebody else proposing the above idea.

– It’s a call for people (You! Blog readers and commenters!) to suggest some ideas for how to write up the above idea in a sensible way so we can have an Arxiv paper on the topic.