Meh, most Bayesian techniques still assume a model. It's more like: Assuming a model M characterized by parameters T and giving rise to data Y, what is P(T|Y,M

Meh, most Bayesian techniques still assume a model. It's more like: Assuming a model M characterized by parameters T and giving rise to data Y, what is P(T|Y,M) To be sure, you can compare the probability of models as well, and there are Bayesian semiparametric techniques, but models are still really important.