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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2602.08427 |
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| _version_ | 1866918328689229824 |
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| author | Marinescu, Marius |
| author_facet | Marinescu, Marius |
| contents | AI has impacted many disciplines and is nowadays ubiquitous. In particular, spatial statistics is in a pivotal moment where it will increasingly intertwine with AI. In this scenario, a relevant question is what relationship spatial statistics models have with machine learning (ML) models, if any. In particular, in this paper, we explore the connections between Kriging and neural networks. At first glance, they may appear unrelated. Kriging - and its ML counterpart, Gaussian process regression - are grounded in probability theory and stochastic processes, whereas many ML models are extensively considered Black-Box models. Nevertheless, they are strongly related. We study their connections and revisit the relevant literature. The understanding of their relations and the combination of both perspectives may enhance ML techniques by making them more interpretable, reliable, and spatially aware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08427 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Connection between Kriging and Large Neural Networks Marinescu, Marius Machine Learning Statistics Theory 86A32, 60G15 AI has impacted many disciplines and is nowadays ubiquitous. In particular, spatial statistics is in a pivotal moment where it will increasingly intertwine with AI. In this scenario, a relevant question is what relationship spatial statistics models have with machine learning (ML) models, if any. In particular, in this paper, we explore the connections between Kriging and neural networks. At first glance, they may appear unrelated. Kriging - and its ML counterpart, Gaussian process regression - are grounded in probability theory and stochastic processes, whereas many ML models are extensively considered Black-Box models. Nevertheless, they are strongly related. We study their connections and revisit the relevant literature. The understanding of their relations and the combination of both perspectives may enhance ML techniques by making them more interpretable, reliable, and spatially aware. |
| title | The Connection between Kriging and Large Neural Networks |
| topic | Machine Learning Statistics Theory 86A32, 60G15 |
| url | https://arxiv.org/abs/2602.08427 |