Learning at the Edge: Tailed-Uniform Sampling for Robust Simulation-Based Inference

Abstract

We introduce the \textsc{Tailed-Uniform} proposal distribution for generating training simulations in simulation-based inference. Instead of sampling parameters uniformly within bounded regions, we extend the distribution beyond prior boundaries with smooth Gaussian tails. This eliminates sharp discontinuities that cause neural posterior estimators to fail when the posterior distribution intersects or extends beyond the prior bounds. The method requires minimal hyperparameter tuning, with tail widths of 10--30\% of the prior width proving robust across problems. We demonstrate these benefits on a synthetic Gaussian linear task and cosmological parameter inference from the matter power spectrum. We also find that \tail-trained models outperform \textsc{Uniform} ones near the boundaries across various training set sizes and dimensions of the parameter space. This advantage grows in higher dimensions, where boundaries dominate parameter space volume. All code is publicly available on Github at https://github.com/chaipattira/tailed-uniform-sbi.