In this paper we present an overview of classical results about the variance reduction technique of control variates. We emphasize aspects of the theory that are of importance to the practitioner, as well as presenting relevant applications.

SELECTION OF SIMULATION VARIANCE REDUCTION TECHNIQUES THROUGH A FUZZY EXPERT SYSTEM Adrian Aderemi Adewunmi, BSc In this thesis, the design and development of a decision support system for the selection of a variance reduction technique for discrete event simulation studies is presented. In addition, the performance of variance reduction techniques as stand alone and combined application has be...

We develop strategies for integrated use of certain well-known variance reduction techniques to estimate a mean response in a finite-horizon simulation experiment. The building blocks for these integrated variance reduction strategies are the techniques of conditional expectation, correlation induction (including antithetic variates and Latin hypercube sampling), and control variates; and all p...

Appendix A. Algorithms In this appendix we specify the algorithms used in the experiments reported in §5. The algorithms are implementations of the procedures developed in §4. All algorithms are stated for the case where at most q control variates are used for any system, but this includes the case q = 0 where control variates are not used, as explained in §4.2. The algorithms are constructed f...

Appendix A. Algorithms In this appendix we specify the algorithms used in the experiments reported in §5. The algorithms are implementations of the procedures developed in §4. All algorithms are stated for the case where at most q control variates are used for any system, but this includes the case q = 0 where control variates are not used, as explained in §4.2. The algorithms are constructed f...

The method of control variates has been intensively used for reducing the variance of estimated (linear) regression metamodels in simulation experiments. In contrast to previous studies, this article presents a procedure for applying multiple control variates when the objective is to estimate and validate a nonlinear regression metamodel for a single response, in terms of selected decision vari...

We propose new control variates for variance reduction in the Metropolis–Hastings algorithm. We use variates that are functions of both the current state of the Markov chain and the proposed new state. This enable us to specify control variates which have known mean values for general target and proposal distributions. We develop the ideas for both the standard Metropolis–Hastings algorithm and...

A general methodology is presented for the construction and effective use of control variates for reversible MCMC samplers. The values of the coefficients of the optimal linear combination of the control variates are computed, and adaptive, consistent MCMC estimators are derived for these optimal coefficients. All methodological and asymptotic arguments are rigorously justified. Numerous MCMC s...