In mathematics, more specifically in the theory of Monte Carlo methods, variance reduction is a procedure used to increase the precision of the estimates that can be obtained for a given number of iterations. Every output random variable from the simulation is associated with a variance which limits the precision of the simulation results. In order to make a simulation statistically efficient, i.e., to obtain a greater precision and smaller confidence intervals for the output random variable of interest, variance reduction techniques can be used. The main ones are: Common random numbers, antithetic variates, control variates, importance sampling and stratified sampling. Under these headings are a variety of specialized techniques; for example particle transport simulations make extensive use of "weight windows" and "splitting/Russian roulette" techniques, which is a form of importance sampling.
Read more about Variance Reduction: Common Random Numbers (CRN), Underlying Principle of The CRN Technique
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