Yes, however as I said in the subsequent sentence, the approximation is "often far more than other methods". For the most obvious example, a point estimate like

Yes, however as I said in the subsequent sentence, the approximation is "often far more than other methods". For the most obvious example, a point estimate like MAP doesn't need to compute the denominator in Bayes law. That's two (generally easier) terms to approximate rather than three. Those using Bayesian methods point out the value in providing a full distribution, but the necessary additional approximations to get it means the location of its maximum can now actually be less accurate than a simple MAP estimate. But what always bugged me is multivariate problems where the Bayesian paper presumes everything is independent and Gaussian. Great after getting all psyched by that intro talk about the value of getting a distribution, we get the simplest imaginable one, just a mean and variance for each variable.