Variance Reduction Techniques

This lecture covers variance reduction techniques in stochastic simulation, including control variates and stratification. The goal is to reduce the variance of simulation estimators by exploiting correlations between variables. Strategies such as generating independent samples and using crude Monte Carlo methods are discussed. The instructor explains the concept of control variates, which involves selecting an auxiliary variable highly correlated with the target variable. Various formulas and methods are presented to achieve variance reduction, aiming to improve the accuracy and efficiency of simulation results.