On the convergence of quasi-random sampling importance resampling

On the convergence of quasi-random sampling/importance resampling

Convergence Properties of Crystal Structure Prediction by Quasi-Random Sampling

Generating sets of trial structures that sample the configurational space of crystal packing possibilities is an essential step in the process of ab initio crystal structure prediction (CSP). One effective methodology for performing such a search relies on low-discrepancy, quasi-random sampling, and our implementation of such a search for molecular crystals is described in this paper. Herein we...

Quasi Importance Sampling

There arise two problems when the expectation of some function with respect to a nonuniform multivariate distribution has to be computed by (quasi-) Monte Carlo integration: the integrand can have singularities when the domain of the distribution is unbounded and it can be very expensive or even impossible to sample points from a general multivariate distribution. We show that importance sampli...

Financial Modeling Using Sampling Importance Resampling"

While Bayesian methodology has been for decades widely applied to econo­ metric models, robust methods had just lately gained more attention of econo­ metricians. More recently, simulation-based techniques, such as the Sampling­ Importance Resampling (SIR) algorithm, have become useful and popular ap­ proaches to statistical problems. This article puts together the robust and Bayesian approache...

Quasi-Random Sampling for Condensation

The problem of tracking pedestrians from a moving car is a challenging one. The Condensation tracking algorithm is appealing for its generality and potential for real-time implementation. However, the conventional Condensation tracker is known to have di culty with high-dimensional state spaces and unknown motion models. This paper presents an improved algorithm that addresses these problems by...