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.

This paper presents a technique that exploits the denoised speech’s variance, estimated during the speech feature enhancement process, to improve noise-robust speech recognition. This technique provides an alternative to the Bayesian predictive classification decision rule by carrying out an integration over the feature space instead of over the model-parameter space, offering a much simpler sy...

This paper presents an analytical view of variance reduction by control variate technique for pricing arithmetic Asian options as a financial derivatives. In this paper, the effect of correlation between two random variables is shown. We propose an efficient method for choose suitable control in pricing arithmetic Asian options based on the control variates (CV). The numerical experiment shows ...

Monte-Carlo Tree Search (MCTS) has proven to be a powerful, generic planning technique for decision-making in single-agent and adversarial environments. The stochastic nature of the Monte-Carlo simulations introduces errors in the value estimates, both in terms of bias and variance. Whilst reducing bias (typically through the addition of domain knowledge) has been studied in the MCTS literature...

Quantiles, which are also known as values-at-risk in finance, are often used as risk measures. Latin hypercube sampling (LHS) is a variance-reduction technique (VRT) that induces correlation among the generated samples in such a way as to increase efficiency under certain conditions; it can be thought of as an extension of stratified sampling in multiple dimensions. This paper develops asymptot...

SUMMARY Multivariate normal or Student importance sampling is a commonly used technique for integration problems in statistical inference. This integration approach is easy to implement, has straightforward error estimates and is eeective in a number of problems. A variety of variance reduction techniques can be considered with importance sampling. Stratiied sampling is one of these and in fact...

Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months. Statistical sampling and sample techniques like SimPoint that pick small sets of execution samples have been shown to provide accurate results while significantly reducing simulation time. The inefficiencies in sampling are (a) ne...

We consider a hyperbolic system with uncertainty in the boundary and initial data. Our aim is to show that different boundary conditions gives different convergence rates of the variance of the solution. This means that we can with the same knowledge of data get a more or less accurate description of the uncertainty in the solution. A variety of boundary conditions are compared and both analyti...

An alternative to the classical mixedmodel with normal random effects is to use a Dirichlet process to model the random effects. Such models have proven useful in practice, and we have observed a noticeable variance reduction, in the estimation of the fixed effects, when the Dirichlet process is used instead of the normal. In this paper we formalize this notion, and give a theoretical justifica...