In this chapter we explain variance reduction techniques from the Hilbert space standpoint, in the terminating simulation context. We use projection ideas to explain how variance is reduced, and to link different variance reduction techniques. Our focus is on the methods of control variates, conditional Monte Carlo, weighted Monte Carlo, stratification, and Latin hypercube sampling.

This paper examines the stochastic behavior of the 1-month interbank rate in ten countries. Various one-factor models are estimated using an exact maximum likelihood estimator, which is based on the recently introduced Gaussian methodology. Interest rate volatility is found to be less sensitive to interest rate levels than stated in the literature. In addition, the constant elasticity variance ...

Agent evaluation in stochastic domains can be difficult. The commonplace approach of Monte Carlo evaluation can involve a prohibitive number of simulations when the variance of the outcome is high. In such domains, variance reduction techniques are necessary, but these techniques require careful encoding of domain knowledge. This paper introduces baseline as a simple approach to creating low va...

The subject of this paper is IddQ testing for deep submicron CMOS technologies. The key concept introduced is the need to reduce the variance of good and faulty IddQ distributions. Other IddQ based techniques are reviewed within the context of variance reduction. Using the SEMATECH data and production data, variance reduction techniques are demonstrated. The main contribution of the paper is th...