Why is a Bayesian not allowed to look at the residuals? In the article "Discussion: Should Ecologists Become Bayesians?" Brian Dennis gives a surprisingly balanced and positive view of Bayesian statistics when his aim seems to be to warn people about it. However, in one paragraph, without any citations or justifications, he says:

Bayesians, you see, are not allowed to look at their residuals. It
  violates the likelihood principle to judge an outcome by how extreme
  it is under a model. To a Bayesian, there are no bad models, just bad
  beliefs.

Why would a Bayesian not be allowed to look at the residuals? What would be the appropriate citation for this (i.e. who is he quoting)?
Dennis, B.
Discussion: Should Ecologists Become Bayesians?
Ecological Applications, Ecological Society of America, 1996, 6, 1095-1103
 A: They can look but not touch. After all, the residuals are the part of the data that don't carry any information about model parameters, and their prior expresses all uncertainty about those—they can't change their prior based on what they see in the data.
For example, suppose you're fitting a Gaussian model, but notice far too much kurtosis in the residuals. Perhaps your prior hypothesis should have been a t-distribution with non-zero probability over low degrees of freedom, but it wasn't—it was effectively a t-distribution with zero probability everywhere except on infinite degrees of freedom. Nothing in the likelihood can result in non-zero probabilities over regions of the posterior density where the prior density is zero. So the notion of continually updating priors based on likelihoods from data doesn't work when the original prior is mis-specified.
Of course if you Google "Bayesian model checking", you'll see this is a parody of actual Bayesian practice; still, it does represent something of a difficulty for Logic of Science-type arguments for the superiority of Bayesianism on philosophical grounds. Andrew Gelman's blog is interesting on this topic.
A: Of course Bayesians can look at the residuals! And of course there are bad models in Bayesian analysis. Maybe a few Bayesians in the 70's supported views like that (and I doubt that), but you will hardly find any Bayesian supporting this view these days.
I didn't read the text, but Bayesians use things like Bayes factors to compare models. Actually, a Bayesian can even compute the probability of a model being true and pick the model which is more likely to be true. Or a Bayesian can average across models, to achieve a better model. Or can use posterior predictive checks. There are a lot of options to check a model and each one may favor one approach or another, but to say that there are no bad models in Bayesian analysis is non-sense.
So, at most, it would be more appropriate to say that in some extreme versions of Bayesianism (extreme versions that almost no one uses in applied settings, by the way) you're not allowed to check your model. But than you could say that in some extreme versions of frequentism you're not allowed to use observational data as well. But why waste time discussing these silly things, when we can discuss if and when, in an applied setting, we should use Bayesian or frequentist methods or whatever? That's what's important, in my humble opinion. 
Update: The OP asked for a reference of someone advocating the extreme version of Bayes. Since I never read any extreme version of Bayes, I can't provide this reference. But I'd guess that Savage may be such a reference. I never read anything written by him, so I may be wrong. 
ps.: Think about the problem of the "well-calibrated Bayesian" (Dawid (1982), JASA, 77, 379). A coherent subjectivist Bayesian forecaster can't be uncalibrated, and so wouldn't review his model/forecasts despite any overwhelming evidence that he's uncalibrated. But I don't think anyone in practice can claim to be that coherent. Thus, model review is important.
ps2.: I like this paper by Efron as well. The full reference is: Efron, Bradley (2005). "Bayesians, frequentists, and scientists." Journal of the American Statistical Association 100(469).
