Single‐pass stratified importance resampling

Importance Resampling for BSSRDF

Rendering translucent effects using the diffusion model BSSRDF [Jensen et al. 2001] is still a difficult problem compared to traditional BSDF, because it introduces additional degrees of freedom. In a BSSRDF, the light does not scatter at a single point: it enters the surface at one point, and exits at another. Various techniques have been proposed to sample an exit point based on a entry point...

IMPORTANCE RESAMPLING FOR GLOBAL ILLUMINATION by Justin

IMPORTANCE RESAMPLING FOR GLOBAL ILLUMINATION Justin F. Talbot Department of Computer Science Master of Science This thesis develops a generalized form of Monte Carlo integration called Resampled Importance Sampling. It is based on the importance resampling sample generation technique. Resampled Importance Sampling can lead to significant variance reduction over standard Monte Carlo integration...

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...

Stratified Sampling with Spherically Symmetric Importance Samplers

SUMMARY Multivariate normal or Student importance sampling is a commonly used technique for integration problems in statistical inference. This integration approach is easy to implement, has straightforward error estimates and is eeective in a number of problems. A variety of variance reduction techniques can be considered with importance sampling. Stratiied sampling is one of these and in fact...

On the convergence of quasi-random sampling/importance resampling