I am trying to sample a set of points with a given, e.g., a Gaussian distribution, while the points have a minimum distance, $d_{\text{min}}$. The most accurate (but slow) way I found is the following: I generate a number of points, let's say 100, with a Gaussian weight, and then check all the pairs; if only one pair fails to satisfy the minimum distance, the whole set should be discarded and regenerated from scratch. When the $d_{\text{min}}$ is large this method is very inefficient.
I found the Poisson Disk Sampling, which works for uniformly distributed points. I can use the method to increase the speed in search between pairs, but it does not really help to increase the efficiency.