[eximius]

My favorite explanation is that frequentist methods answer the question "If I assume a model, A, what is the likelihood this data came from it?" while bayesian methods answer "Given this data, what is the model?"

Frequentist methods rarely directly answer the question we actually have. But they're generally far easier to compute. Bayesian methods are often much more intuitive but are far more complicated and less performant.

[gbrown]

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.