Monte Carlo simulation methods apply a random sampling and modifications can be made of this method is by using variance reduction techniques (VRT). VRT objective is to reduce the variance due to Monte Carlo methods become more accurate with a variance approaching zero and the number of samples approaches infinity, which is not practical in the real situation (Chen, 2004). These techniques are ...

This paper introduces a new BRDF sampling method with reduced variance, which is based on a hierarchical adaptive PDF. This PDF also is based on rejection sampling with a bounded average number of trials, even in regions where the BRDF exhibits high variations. Our algorithm works in an appropiate way with both physical, analytical and measured reflectance models. Reflected directions are sampl...

We consider the inverse scattering problem of determining the shape of an infinite cylinder having an open arc as cross section from a knowledge of the TM-polarized scattered electromagnetic field corresponding to time-harmonic incident plane waves propagating from arbitrary directions. We assume that the arc is a (possibly) partially coated perfect conductor and develop the linear sampling met...

We discuss using the semi-regenerative method, importance sampling, and stratification to estimate the expected cumulative reward until hitting a fixed set of states for a discrete-time Markov chain on a countable state space. We develop a general theory for this problem and present several central limit theorems for our estimators. We also present some empirical results from applying these tec...

(Received December, 1988) The adaptive umbrella sampling technique, introduced recently to improve the probability ratio method and found to perform more reliably then the customary harmonic umbrella sampling, is tested and compared with other free energy methods. One of the tests applies the method to a transition involving a chemical change: calculation of the hydration free energy difference...

Researchers in many scientific fields make inferences from individuals to larger groups. For many groups however, there is no list of members from which to take a random sample. Respondent-driven sampling (RDS) is a relatively new sampling methodology that circumvents this difficulty by using the social networks of the groups under study. The RDS method has been shown to provide unbiased estima...

This paper investigates the reduction of variance associated with a simulation output performance measure, using the Sequential Sampling method while applying minimum simulation replications, for a class of JIT (Just in Time) warehousing system called crossdocking. We initially used the Sequential Sampling method to attain a desired 95% confidence interval half width of ± 0.5 for our chosen per...

The bootstrap method is often used for variance estimation in sample surveys with a stratified multistage sampling design. It typically implemented by producing set of weights that made available to users and accounts the complexity Rao–Wu–Yue produce required weights. valid under with-replacement at first stage or fixed-size without-replacement provided first-stage fractions are negligible. So...