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?