Stephen G. Strickland

Learn More
In this paper we describe several computational algorithms useful in studying importance sampling (IS) for Markov chains. Our algorithms compute optimal IS measures and evaluate the estimate variance for a given measure. As knowledge of the optimal IS measure implies knowledge of the quantity to be estimated, our algorithms produce this quantity as a(More)
We develop necessary and sufficient conditions for importance sampling measures to yield estimates with bounded relative error. We use these conditions to examine the properties of existing methods for estimating failure probabilities in highly reliable systems. We then propose a new approach which we show has bounded relative error and is asymp-totically(More)
We consider methods for simultaneously estimating the gradient (resp. sensitivity) of J with respect to a continuous (resp. discrete) parameter 0. While one can always estimate a derivative (or sensitivity) by performing two distinct simulations at different values of O (and forming the estimate (J,g/ – JtI,)/(@' – 8)), here we focus on methods which(More)
  • 1