9

In this post, Andrew Gelman says:

Bayesian inference can make strong claims, and, without the safety valve of model checking, many of these claims will be ridiculous. To put it another way, particular Bayesian inferences are often clearly wrong, and I want a mechanism for identifying and dealing with these problems. I certainly don’t want to return to the circa-1990 status quo in Bayesian statistics, in which it was considered virtually illegal to check your model’s fit to data.

What is Andrew Gelman exactly referring to? What rationale would Bayesians give to consider model checking "illegal"? Isn't this view dogmatic and shortsighted, or are there scholars that still advocate it?

alberto
  • 2,646
  • 16
  • 36
  • 3
    I am certain that this very Gelman quotation has been discussed before on this site - off-hand I can't remember where, though perhaps the site search function can track it down. EDIT: try [Why is a Bayesian not allowed to look at the residuals?](http://stats.stackexchange.com/questions/85605/why-is-a-bayesian-not-allowed-to-look-at-the-residuals) where this exact quote is discussed in the comments, which is why it didn't show up on a site search. – Silverfish Jan 30 '15 at 19:04
  • 1
    (I also think Michael Chernick posted a first-hand anecdote about this somewhere, but this time I *really* can't remember where!) – Silverfish Jan 30 '15 at 19:10
  • :) I just found that very same link on google. Still, I don't think I get the point. I'd like to know how this 90's bayesian worked. E.g: what do you do if you do not check your model? You get your posterior and assume it's the truth? – alberto Jan 30 '15 at 19:21
  • Maybe just e-mail Andrew Gelman..? – Tim Feb 01 '15 at 19:36
  • 2
    First, I don't dare since I don't know how overflooded is his e-mail and the question is not life-or-death but rather intellectual curiosity (though it might help me to get a broader perspective on model checking). And second, I thought someone else might be interested in the answer. – alberto Feb 02 '15 at 13:17
  • 1
    Hi Alberto, I took the liberty to edit your question and start a bounty on it. –  Jun 08 '20 at 17:49
  • Maybe this dogma stems from times when statisticians were regarded as *either* Bayesian *or* frequentist (and maybe it's still a bit, which's spouts these type of questions). Terms Bayesian and frequentist were related to a person rather than the analysis/problem at hand. The type of statistical analysis got regarded as a (subjective) decision depending on whether you are frequentist or Bayesian. I believe that this is nonsense to adhere to, and one should be able to mix it up. – Sextus Empiricus Jun 09 '20 at 09:03
  • 1
    But of course it is also bad to fiddle with your analysis a posteriori based on checks of the fit (which is actually true for both Bayesian and frequentist analysis). Maybe that is where the status quo may have stemmed from as well. Or maybe it is a combination. – Sextus Empiricus Jun 09 '20 at 09:05

0 Answers0