Stochastic Simulation: Low-Discrepancy Point Sets

This lecture covers the concept of low-discrepancy point sets in stochastic simulation, focusing on quantifying the discrepancy of a given family of point sets. The instructor explains how to determine if a point set has low-discrepancy and provides examples of regular lattices and their discrepancies. Various algorithms for constructing low-discrepancy point sets are discussed, along with the unbiased estimators and confidence intervals associated with them.