A computer simulation model is unusual in that the random error is under the total control of
the experimenter. Variance reduction methods aim to take advantage of this to improve …

… Here we survey a few of the most important methods for variance reduction and speedup
that will benefit any simulation, no matter what the capabilities of the computing hardware. …

… In this section, we provide two stochastic variance reduction methods and show they … our
proposed stochastic variance reduction methods dominate other first-order methods. Moreover, …

… Let us assume that it is calculated by some consistent method. If the variance is too large
for … As a final example of electron variance reduction, we consider the technique called "range …

… always) achieved through variance reduction. We discuss methods such as common random
… combination of certain of those methods. We also survey the recent literature on this topic. …

… variance reduction methods because they do not bias the random walk sampling. Standard
“production” Monte Carlo codes such as MCNP (Briesmeister, 2000), MORSE (Emmett, 1975…

… The proposed method is based on an information response function (ie, the amount of …
method was later applied to groundwater data management problems. Total variance reduction (a …

… Monte Carlo methods have attracted constant and even … its related, especially variance
reduction, techniques through a … developments of existing numerical methods, ranging from the …

… This section develops a method of importance sampling to reduce the variance arising
from the Monte-Carlo integration of f: d C([0, T]; d) with respect to the multidimensional Wiener …

… results on variance reduction. The key idea used by previous authors is to show that the
variance of the … Our proof generalizes to all variance-reduction methods. However, for brevity we …