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Kriging and Gaussian processes: interconnected frameworks for spatial modelling and prediction — arXiv:2610.10783

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10783
License (as archived): CC0-1.0
Archived: 2026-10-09T06:27:36.382Z
SHA-256 of the archived text: 85fbdd130549255bb03097008db9109174fa7c0c42050516a09e00d2ab4bd999
Stable URL of this page: https://metraipolis.com/documento/dc_8ff1ff4edebe

How to cite

Kriging and Gaussian processes: interconnected frameworks for spatial modelling and prediction — arXiv:2610.10783 — arXiv (en), https://arxiv.org/abs/2610.10783 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8ff1ff4edebe (Metraipolis archive dc_8ff1ff4edebe, sha256 85fbdd130549255bb03097008db9109174fa7c0c42050516a09e00d2ab4bd999)

BibTeX

@misc{dc8ff1ff4edebe,
  title = {Kriging and Gaussian processes: interconnected frameworks for spatial modelling and prediction — arXiv:2610.10783},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10783},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8ff1ff4edebe; sha256 85fbdd130549255bb03097008db9109174fa7c0c42050516a09e00d2ab4bd999},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10783. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Spatial modelling and inference arise across many areas of science and engineering, with Kriging and Gaussian process (GP) regression serving as two widely used predictive models. Although grounded in the same random field theory, these approaches are often treated in isolation. As a result, their connection has been obscured by differences in terminology, notation, and practical implementation. This separation largely reflects their independent development within geostatistics and machine learning, respectively. To bridge this methodological divide, this work establishes a unified mathematical framework that connects the two paradigms. The underlying assumptions and formulations of each approach are detailed and compared within this framework. This comparison highlights their differences while clarifying the connections between them. The resulting perspective provides practitioners with a common foundation for translating concepts and methods between geostatistics and machine learning. In particular, it makes methodological developments in either field more accessible to the other, including advances in uncertainty quantification from the GP literature.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2610.10783 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T05:51:46.597Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).