A central limit theorem for refined descriptive sampling

Refined Descriptive Sampling (RDS) is one of the best methods that can be used in Monte Carlo simulation especially when the precision of the estimates is important. The RDS method provides lower-variance and more accurate estimates of multidimensional integrals than the standard Monte Carlo (MC), Descriptive Sampling, and Latin Hypercube Sampling (LHS) methods. In this paper, we focused first, on the practical Performance of the RDS Method by its comparison with MC and LHS multidimensional integral estimators, and second, on the use of RDS in the limit of error estimation to the Normal distribution which led to the proposal of the central limit theorem under RDS.