934 resultados para RANDOM CONDUCTANCES


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gsample draws a random sample from the data in memory. Simple random sampling (SRS) is supported, as well as unequal probability sampling (UPS), of which sampling with probabilities proportional to size (PPS) is a special case. Both methods, SRS and UPS/PPS, provide sampling with replacement and sampling without replacement. Furthermore, stratified sampling and cluster sampling is supported.

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The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.

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National Highway Traffic Safety Administration, Office of Driver and Pedestrian Programs, Washington, D.C.

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Transportation Department of Office of University Research, Washington, D.C.

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National Highway Traffic Safety Administration, Office of Driver and Pedestrian Programs, Washington, D.C.

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A chapter supplementary to the author's Choice and Chance.

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Vita.

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"Errata" ([3] p.) inserted.

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"UIUCDCS-R-74-679"

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"COO 1469-0209."