I have a problem understanding why Bayesian Inference leads to intractable problems. The problem is often explained like this:
What I don't understand is why this integral has to be evaluated in the first place: It seems to me that the result of the integral is simply a normalization constant (as the dataset D is given). Why can one not simply calculate the posterior distribution as the numerator of the right-hand side and then infer this normalization constant by requiring that the integral over the posterior distribution has to be 1?
What am I missing?