The control variates method is a variance reduction technique used in Monte Carlo methods. It exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknown quantity. Underlying principle Let the unknown parameter of interest be , and assume we have a statistic such that the expected value of m is μ: , i.e. m is an unbiased estimator for μ. Suppose we calculate another statistic such that is a known value. Then is also an unbiased estimator for for any choice of the coefficient . The variance of the resulting estimator is By differentiating the above expression with respect to , it can be shown that choosing the optimal coefficient minimizes the variance of , and that with this choice, where is the correlation coefficient of and . The greater the value of , the greater the variance reduction achieved. In the case that , , and/or are unknown, they can be estimated across the Monte Carlo replicates. This is equivalent to solving a certain least squares system; therefore this technique is also known as regression sampling. When the expectation of the control variable, , is not known analytically, it is still possible to increase the precision in estimating (for a given fixed simulation budget), provided that the two conditions are met: 1) evaluating is significantly cheaper than computing ; 2) the magnitude of the correlation coefficient is close to unity. Example We would like to estimate using Monte Carlo integration. This integral is the expected value of , where and U follows a uniform distribution [0, 1]. Using a sample of size n denote the points in the sample as . Then the estimate is given by Now we introduce as a control variate with a known expected value and combine the two into a new estimate Using realizations and an estimated optimal coefficient we obtain the following results The variance was significantly reduced after using the control variates technique. (The exact result is .) See also Antithetic variates Importance sampling Notes References Ross, Sheldon M. (2002) Simulation 3rd edition Averill M. Law & W. David Kelton (2000), Simulation Modeling and Analysis, 3rd edition. S. P. Meyn (2007) Control Techniques for Complex Networks, Cambridge University Press. . Downloadable draft (Section 11.4: Control variates and shadow functions) Monte Carlo methods Statistical randomness Computational statistics Variance reduction