Variance Reduction Techniques

Variance Reduction Techniques in . . .

Variance Reduction Techniques in Monte Carlo Methods

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develo...

Non-Linear Variance Reduction Techniques in Biometric Authentication

In this paper, several approaches that can be used to improve biometric authentication applications are proposed. The idea is inspired by the ensemble approach, i.e., the use of several classifiers to solve a problem. Compared to using only one classifier, the ensemble of classifiers has the advantage of reducing the overall variance of the system. Instead of using multiple classifiers, we prop...

Variance Reduction Techniques for Estimating Value-at-Risk

T paper describes, analyzes and evaluates an algorithm for estimating portfolio loss probabilities using Monte Carlo simulation. Obtaining accurate estimates of such loss probabilities is essential to calculating value-at-risk, which is a quantile of the loss distribution. The method employs a quadratic (’’delta-gamma’’) approximation to the change in portfolio value to guide the selection of e...

Advanced Variance Reduction Techniques 3 The General Formulation

In these lecture notes we cover the more advanced variance reduction techniques of importance sampling and stratified sampling. Importance sampling is particularly suited for estimating the probabilities of rare events and, in conjunction with stratified sampling, often results in a sample variance that is several orders of magnitude smaller than the variance of the naive Monte-Carlo estimator....