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- W2962868312 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.
- W2962868312 created "2019-07-30" @default.
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- W2962868312 date "2020-01-01" @default.
- W2962868312 modified "2023-09-24" @default.
- W2962868312 title "A class of multi-resolution approximations for large spatial datasets" @default.
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- W2962868312 doi "https://doi.org/10.5705/ss.202018.0285" @default.
- W2962868312 hasPublicationYear "2020" @default.
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