One very influential view on Bayesian statistics buys into the subjectivity of the analysis (e.g., Goldstein 2006, Kadane 2011). So-called personalists or strict subjectivists argue that it is just right that the statistical methods do not provide any objective guidelines, pointing to radically subjective sources of any form of knowledge. The problems on the interpretation and choice of the prior distribution are thus dissolved, at least in part: the Bayesian statistician may choose her prior at will, and they are an expression of her beliefs. However, it deserves emphasis that a subjectivist view on Bayesian statistics does not mean that all constraints deriving from empirical fact can be disregarded. Nobody denies that if you have further knowledge that imposes constraints on the model or the prior, then those constraints must be accommodated. For example, today's posterior probability may be used as tomorrow's prior, in the next statistical inference. The point is that such constraints concern the rationality of belief and not the consistency of the statistical inference per se.