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2006 IEEE Symposium on Interactive Ray Tracing, 2006
High quality ray tracing requires pixel sampling and filtering for antialiasing, as well as illumination sampling for complex lighting effects. Both problems are well understood and sophisticated sampling techniques are available for each of them. However, it turns out that weighted pixel sampling spoils the benefits of clever illumination sampling ...
Manfred Ernst +2 more
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High quality ray tracing requires pixel sampling and filtering for antialiasing, as well as illumination sampling for complex lighting effects. Both problems are well understood and sophisticated sampling techniques are available for each of them. However, it turns out that weighted pixel sampling spoils the benefits of clever illumination sampling ...
Manfred Ernst +2 more
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Asymptotic importance sampling
Structural Safety, 1993Abstract An importance sampling technique is described which is based on theoretical considerations about the structure of multivariate integrands in domains having small probability content. The method is formulated in the original variable space.
Marc A. Maes +2 more
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ACM SIGGRAPH 2013 Talks, 2013
Light propagation within translucent materials can be described by a BSSRDF [Jensen et al. 2001]. The main difficulty in integrating this effect lies in the generation of well-distributed samples on the surface within the support of the rapidly decaying BSSRDF profile.
Alan King +3 more
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Light propagation within translucent materials can be described by a BSSRDF [Jensen et al. 2001]. The main difficulty in integrating this effect lies in the generation of well-distributed samples on the surface within the support of the rapidly decaying BSSRDF profile.
Alan King +3 more
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ACM Transactions on Graphics, 2005
We present a new technique for importance sampling products of complex functions using wavelets. First, we generalize previous work on wavelet products to higher dimensional spaces and show how this product can be sampled on-the-fly without the need of evaluating the full product.
Petrik Clarberg +3 more
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We present a new technique for importance sampling products of complex functions using wavelets. First, we generalize previous work on wavelet products to higher dimensional spaces and show how this product can be sampled on-the-fly without the need of evaluating the full product.
Petrik Clarberg +3 more
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Independent importance sampling
2000Abstract We use the term independent importance sampling to refer to what might be considered the most straightforward Monte Carlo integration technique; namely generating independent values x1, ... , Xn from a density w and then taking a weighted average of the integrand evaluated at the sampled points with the weights given by the ...
Michael Evans, Tim Swartz
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Optimise importance sampling quantile estimation
Biometrika, 1996This paper considers the use of an auxiliary variable X* to estimate quantiles of a test statistic X; X* may be an asymptotic expansion of X, or a simplified version which ignores some of the convariance structure. The proposed estimator involves three stages. First a large sample is drawn, and X* is evaluated. Then a first subsample is drawn, X and X*
Goffinet, Bruno, Wallach, Daniel
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Nonparametric Importance Sampling
Journal of the American Statistical Association, 1996Abstract Importance sampling is a widely used variance reduction simulation technique for the evaluation of high-dimensional integrals. A key step in the implementation of importance sampling is to choose a proper distribution function from which pseudorandom numbers are generated.
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Multiple Importance Sampling for PET
IEEE Transactions on Medical Imaging, 2014This paper proposes the application of multiple importance sampling in fully 3-D positron emission tomography to speed up the iterative reconstruction process. The proposed method combines the results of lines of responses (LOR) driven and voxel driven projections keeping their advantages, like importance sampling, performance and parallel execution on
Laszló, Szirmay-Kalos +2 more
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Journal of Computational and Graphical Statistics, 2008
Importance sampling is a fundamental Monte Carlo technique. It involves generating a sample from a proposal distribution in order to estimate some property of a target distribution. Importance sampling can be highly sensitive to the choice of proposal distribution, and fails if the proposal distribution does not sufficiently well approximate the target.
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Importance sampling is a fundamental Monte Carlo technique. It involves generating a sample from a proposal distribution in order to estimate some property of a target distribution. Importance sampling can be highly sensitive to the choice of proposal distribution, and fails if the proposal distribution does not sufficiently well approximate the target.
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Generalised Importance sampling
Forest Ecology and Management, 1996Abstract Importance sampling and Centroid sampling are efficient methods of estimating tree stem volume, and the techniques are very useful when good tree volume equations do not exist. Each method requires diameter measurements at two points up a tree stem (generally, but not necessarily, at 1.3 m and tree height), and a proxy taper function, to ...
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