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Spatial mapping with Gaussian processes and nonstationary Fourier features
The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple linear additive methods to nonlinear methods with higher order interactions. However, until recently, there has been a stron...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier B.V
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472673/ https://www.ncbi.nlm.nih.gov/pubmed/31008043 http://dx.doi.org/10.1016/j.spasta.2018.02.002 |