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• W3037531075 startingPage "383" @default.
• W3037531075 abstract "Gaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussian-process approximations that use basis functions at multiple resolutions to achieve fast inference and that can (approximately) represent any spatial covariance structure. We consider two special cases of this multi-resolution-approximation framework, a taper version and a domain-partitioning (block) version. We describe theoretical properties and inference procedures, and study the computational complexity of the methods. Numerical comparisons and an application to satellite data are also provided." @default.
• W3037531075 created "2020-07-02" @default.
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• W3037531075 date "2020-06-23" @default.
• W3037531075 modified "2023-10-17" @default.
• W3037531075 title "Vecchia Approximations of Gaussian-Process Predictions" @default.
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• W3037531075 doi "https://doi.org/10.1007/s13253-020-00401-7" @default.
• W3037531075 hasPublicationYear "2020" @default.
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