KATIE is a parton-level event generator for hadron scattering processes that can deal with partonic initial-state momenta with an explicit transverse momentum dependence causing them to be space-like. Provided with the necessary transverse momentum dependent parton density functions, it calculates the tree-level off-shell matrix elements and performs the phase space importance sampling to produ...

Abstract: In this paper we consider estimation of R = P (Y < X), when X and Y are distributed as two independent four-parameter generalized gamma random variables with same location and scale parameters. A modified maximum likelihood method and a Bayesian technique have been used to estimate R on the basis of independent samples. As the Bayes estimator cannot be obtained in a closed form, it ha...

The concept of simulating a variant of QCD with sea quarks which interact with the gluon configuration only via global gluonic quantities like ∫ FF̃ dx and ∫ FF dx is tested for the case of the massive 2-flavour Schwinger model. It is found to amount to an importance sampling method which generates –at essentially the costs of a quenched run– an ensemble in between a full and a quenched one, thu...

The determination of packet loss rates in multiplexers is an important problem that has received much attention in the literature. This paper considers the problem of estimating, via simulation, extremely low packet loss rates in a voice-data multiplexer. The multiplexer gives priority to voice packets, unless the data queue length exceeds a threshold. To eeciently simulate such low loss rates ...

The evaluation of the likelihood function of the stochastic conditional duration model requires to compute an integral that has the dimension of the sample size. We apply the efficient importance sampling method for computing this integral. We compare EIS-based ML estimation with QML estimation based on the Kalman filter. We find that EIS-ML estimation is more precise statistically, at a cost o...

I present a simple variation of importance sampling that explicitly searches for important regions in the target distribution. I prove that the technique yields unbiased estimates, and show empirically it can reduce the variance of standard Monte Carlo estimators. This is achieved by concentrating samples in more significant regions of the sample space.

In this paper, we present a new technique for detecting moving targets from image sequences captured by moving sensors. Feature points are detected and tracked through the image sequences. A validity vector is used to describe the consistency of feature trajectories with sensor motion. By using the sequential importance sampling method, an approximation to the posterior distribution of the sens...

In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are obtained with logistic regression using the corresponding hard rules. Soft rules are useful when both the response and the input variables are continuous because the soft rules p...

Despite recent advances in Monte Carlo rendering techniques, dense, high-albedo participating media such as wax or skin still remain a difficult problem. In such media, random walks tend to become very long, but may still lead to a large contribution to the image. The Dwivedi sampling scheme, which is based on zero variance random walks, biases the sampling probability distributions to exit the...

This article provides importance sampling algorithms for computing the probabilities of various types ruin of spectrally negative Lévy risk processes, which are ruin over the infinite time horizon, ruin within a finite time horizon and ruin past a finite time horizon. For the special case of the compound Poisson process perturbed by diffusion, algorithms for computing probabilities of ruins by ...