>However, if all else isn't equal and you are well acquainted with a Mr Bayes you can probably do all sorts of fancy Mathematics to estimate which model is more

>However, if all else isn't equal and you are well acquainted with a Mr Bayes you can probably do all sorts of fancy Mathematics to estimate which model is more likely to be true. But you still need to tell Mr Bayes what your criteria are for model-selection. So the output of your calculations is some scalar "truth-value" (higher is better) - what's your input? In simpler terms: the truthfulness of your model is a function of... what ? >Mind you, I don't think this is an issue for most subjects. How often do you get different functions that spew the same output in the all observable and theoretical instances? In curve fitting? Practically all the time! It is a mathematical fact that infinitely many curves fit a finite dataset. So we pick curves that fit approximately, not exactly with the obvious underfitting/overfitting optimisation problem that needs solving in conjunction.