That's what Jaynes did to achieve his awesome victories: use trained intuition to pick good priors by hand on a per-sample basis.

... as if applying the classical method doesn't require using trained intuition to use the "right" method for a particular kind of problem, which amounts to choosing a prior but doing it implicitly rather than explicitly ...

Our inference is conditional on our assumptions [for example, the prior P(Lambda)]. Critics view such priors as a difficulty because they are `subjective', but I don't see how it could be otherwise. How can one perform inference without making assumptions? I believe that it is of great value that Bayesian methods force one to make these tacit assumptions explicit.

McKay, information theory, learning and inference