Bounds of star discrepancy for HSFC-based sampling

In this paper, we focus on estimating the probabilistic upper bounds of star discrepancy for Hilbert space filling curve (HSFC) sampling. The main idea is the stratified random sampling method, but the strict condition for sampling number $N=m^d$ of jittered sampling is removed. We inherit the advantages of this sampling and get better results than Monte Carlo (MC) sampling.