I posted the following as a comment on the article, but it's stuck in moderation there... I'm not clear on what it is that you [the post's author] don't unders

I posted the following as a comment on the article, but it's stuck in moderation there... I'm not clear on what it is that you [the post's author] don't understand about interpretation of Bayesian credible intervals. Both "objective" and "subjective" Bayesians interpret them as degrees of belief - that, for instance, one would use to make bets (supposing, of course, that you have no moral objection to gambling, etc.). The difference is that that "objective" Bayesians think that one can formalize "what one knows" and then create an "objective" prior on that basis, that everyone "with the same knowledge" would agree is correct. I don't buy this. Formalizing "what one knows" by any means other than specifying a prior (which would defeat the point) seems impossible. And supposing one did, there is disagreement about what an "objective" prior based on it would be. To joke, "The best thing about objective priors is there are so many of them to choose from!". Many simple examples can illustrate that the objective Bayesian framework just isn't going to work. One example is the one-way random effects model, where the prior on the variance of the random effects will sometimes have a large influence on the inference (eg, on the posterior probability that the overall mean is positive), but where there is no sensible "objective" prior - you just have to subjectively specify how likely it is that the variance is very close to zero. Another even simpler example is inference for theta given an observation x~N(theta,1), when it is known (with certainty) that theta is non-negative, and the observed x is -1. There's just no alternative to subjectively deciding how likely a priori it is that theta is close to zero. Frequentist methods also don't give sensible answers in these examples. Subjective Bayesianism is the only way.