Hidden low-discrepancy structures in random point sets

Abstract: We study the probabilistic existence of point configurations satisfying the $(0, m, d)$-net property in base $b$ within a randomly generated point set in the $d$-dimensional unit cube. We first derive an upper bound on the number of geometric patterns for $(0, m, d)$-nets in base $b$. By applying the concentration inequalities together with this bound, we give lower and upper estimates for the probability that a set of $N$ random points contains a $(0, m, d)$-net as a subset. This result leads to necessary and sufficient scaling conditions on $N$ and $m$ such that this probability converges to $1$.