**Moncy Vilavinal John wrote:**

I agree with this - but it's *NOT* what happens in model selection. Your original prior is never forgotten. Let's say your prior was n_s varies between 0 and 1000 with a uniform probability. Then you make a measurement which yields a posterior that is a Gaussian peak near n_s=1 with width 0.1. You might want to adopt this Gaussian as the prior for the next time you do model comparison, but the question is are you allowed to renormalise the probability under it to unity, rather than 0.0001? If you do so, then the Evidence ratio always ends up just as the likelihood ratio. This seems sensible to me, but it's not the official approach.As I understand, the authors and also Peacock are worried about whether a competing model in a model comparison can get undue advantage by picking a suitable prior for some new parameter. But this anxiety is unfounded and can be dispelled once we recognize that Bayesian model comparison is not a one-time exercise. The posterior for that parameter, obtained in that analysis, must be used as prior in the future observation