Sure, the likelihoods are the same in both cases, since A and B’s probability distributions assign the same probability to any sequence that is in both of their supports. But the distributions are still different, and various functionals of them are still different—e.g., the number of tails, the moments (if we convert heads and tails to numbers), etc.
If you’re a Bayesian, you think any hypothesis worth considering can predict a whole probability distribution, so there’s no reason to worry about these functionals when you can just look at the probability of your whole data set given the hypothesis. If (as in actual scientific practice, at present) you often predict functionals but not the whole distribution, then the difference in the functionals matters. (I admit that the coin example is too basic here, because in any theory about a real coin, we really would have a whole distribution.)
My point is just that there are differences between the two cases. Bayesians don’t think these differences could possibly matter to the sort of hypotheses they are interested in testing, but that doesn’t mean that in principle there can be no reason to differentiate between the two.
Sure, the likelihoods are the same in both cases, since A and B’s probability distributions assign the same probability to any sequence that is in both of their supports. But the distributions are still different, and various functionals of them are still different—e.g., the number of tails, the moments (if we convert heads and tails to numbers), etc.
If you’re a Bayesian, you think any hypothesis worth considering can predict a whole probability distribution, so there’s no reason to worry about these functionals when you can just look at the probability of your whole data set given the hypothesis. If (as in actual scientific practice, at present) you often predict functionals but not the whole distribution, then the difference in the functionals matters. (I admit that the coin example is too basic here, because in any theory about a real coin, we really would have a whole distribution.)
My point is just that there are differences between the two cases. Bayesians don’t think these differences could possibly matter to the sort of hypotheses they are interested in testing, but that doesn’t mean that in principle there can be no reason to differentiate between the two.