We study frequentist properties of Bayesian and $L_0$ model selection, with a focus on (potentially non-linear) high-dimensional regression. propose construction to how posterior probabilities normalized criteria concentrate the (Kullback-Leibler) optimal other subsets space. When such concentration occurs, one also bounds selecting correct model, type I II errors. These results hold generally,...