Does it, though? If you were going to call that background evidence into question for a mere 10^10-to-1 evidence, should the probability have been 10^100-to-1 against in the first place?
This is verging on the question, what do you do when the truth is not in the support of your model? That may be the main way you reach 10^100-to-1 odds in practice. Non-Bayesians like to pose this question as a knock-down of Bayesianism. I don’t agree with them, but I’m not qualified to argue the case.
Once you’ve accepted some X as evidence, i.e. conditioned all your probabilities on X, how do you recover from that when meeting new evidence Y that is extraordinarily unlikely (e.g. 10 to −100) given X? Pulling X out from behind the vertical bar may be a first step, but that still leaves you contemplating the extraordinarily unlikely proposition X&Y that you have nevertheless observed.
This is verging on the question, what do you do when the truth is not in the support of your model? That may be the main way you reach 10^100-to-1 odds in practice. Non-Bayesians like to pose this question as a knock-down of Bayesianism. I don’t agree with them, but I’m not qualified to argue the case.
Once you’ve accepted some X as evidence, i.e. conditioned all your probabilities on X, how do you recover from that when meeting new evidence Y that is extraordinarily unlikely (e.g. 10 to −100) given X? Pulling X out from behind the vertical bar may be a first step, but that still leaves you contemplating the extraordinarily unlikely proposition X&Y that you have nevertheless observed.