M Carlo simulation is widely used to measure the credit risk in portfolios of loans, corporate bonds, and other instruments subject to possible default. The accurate measurement of credit risk is often a rare-event simulation problem because default probabilities are low for highly rated obligors and because risk management is particularly concerned with rare but significant losses resulting fr...

Recent studies have shown that power systems protection mechanisms have played a major role in propagating disturbances. Out of the last five major Western Systems Coordinating Council (WSCC) events (the North Ridge earthquake, December 14 1994, July 2 & 3 1996, and August 1

Full likelihood inference under Kingman’s coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) method have been applied successfully. Both methods can be expressed in terms of families of intractable conditional sampling distributions (CSDs), and rely on principled approximations for accurate inference. Recently,...

An appearance-based EigenTracker can track objects which simultaneously undergo image motions as well as changes in view. This paper enhances the framework in two ways. First, we incorporate a novel CONDENSATION-based predictive framework to speed up the EigenTracker. Next, our scheme is on-line: we use efficient eigenspace updates to track unknown objects. We use Importance Sampling for enhanc...

We extend a previously developed method, based on Wagner’s stochastic formulation of importance sampling, to the calculation of reaction rates and to a simple quantitative description of finite-temperature, average dynamic paths. Only the initial and final states are required as input—no information on transition state~s! is necessary. We demonstrate the method for a single particle moving on t...

The paper introduces a family of approximate schemes that extend the process of computing sample mean in importance sampling from the conventional OR space to the AND/OR search space for graphical models. All the sample means are defined on the same set of samples and trade time with variance. At one end is the AND/OR sample tree mean which has the same time complexity as the conventional OR sa...

Softmax GAN is a novel variant of Generative Adversarial Network (GAN). The key idea of Softmax GAN is to replace the classification loss in the original GAN with a softmax cross-entropy loss in the sample space of one single batch. In the adversarial learning of N real training samples and M generated samples, the target of discriminator training is to distribute all the probability mass to th...

Microfacet models have proven very successful for modeling light reflection from rough surfaces. In this paper we review microfacet theory and demonstrate how it can be extended to simulate transmission through rough surfaces such as etched glass. We compare the resulting transmission model to measured data from several real surfaces and discuss appropriate choices for the microfacet distributi...

A framework for real-time tracking of complex non-rigid objects is presented. The object shape is approximated by an ellipse and its appearance by histogram based features derived from local image properties. An efficient search procedure is used to find the image region with a histogram most similar to the histogram of the tracked object. The procedure is a natural extension of the mean-shift ...

We present a technique to efficiently importance sample distant, all-frequency illumination in indoor scenes. Standard environment sampling is inefficient in such cases since the distant lighting is typically only visible through small openings (e.g. windows). This visibility is often addressed by manually placing a portal around each window to direct samples towards the openings; however, unif...