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• W2007676551 abstract "A single-index model (SIM) provides for parsimonious multidimensional nonlinear regression by combining parametric (linear) projection with univariate nonparametric (nonlinear) regression models. We show that a particular Gaussian process (GP) formulation is simple to work with and ideal as an emulator for some types of computer experiment as it can outperform the canonical separable GP regression model commonly used in this setting. Our contribution focuses on drastically simplifying, reinterpreting, and then generalizing a recently proposed fully Bayesian GP-SIM combination. Favorable performance is illustrated on synthetic data and a real-data computer experiment. Two R packages, both released on CRAN, have been augmented to facilitate inference under our proposed model(s)." @default.
• W2007676551 created "2016-06-24" @default.
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• W2007676551 date "2012-02-01" @default.
• W2007676551 modified "2023-10-16" @default.
• W2007676551 title "Gaussian Process Single-Index Models as Emulators for Computer Experiments" @default.
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• W2007676551 doi "https://doi.org/10.1080/00401706.2012.650527" @default.
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