[usgroup]

If by prior you just mean "I know something about it" or "everything happens in context"; then that fine. But if thats what you mean then a diminishingly small number of events have "priors" which can be expressed in a neat analytical form, or be approximated, or even be quantified. This is part of the problem of frame and context that ML v1.0 tried hard to solve.

Recall as well that in the Bayesian approach the model itself is not subject to Bayesian updating: its part of your prior. Except that you never update it. So youre not merely choosing how to update parameters given data; you're also choosing what you're not going to update.