A large chunk of academics would say that it is. For example, from the paper I was referencing in my post:
At some point in history, a statistician may well write down a model which he or she believes contains all the systematic influences among properly defined variables for the system of interest, with correct functional forms and distributions of noise terms. This could happen, but we have never seen it, and in social science we have never seen anything that comes close. If nothing else, our own experience suggests that however many different specifications we thought of, there are always others which did not occur to us, but cannot be immediately dismissed a priori, if only because they can be seen as alternative approximations to the ones we made. Yet the Bayesian agent is required to start with a prior distribution whose support covers all alternatives that could be considered.
That doesn't at all say Bayesian reasoning assumes only two possibilities. It says Bayesian reasoning assumes you know what all the possibilities are.
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
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