The Connection between Kriging and Large Neural Networks
This work addresses the problem of improving interpretability and reliability in machine learning for researchers and practitioners by linking spatial statistics with AI, though it appears incremental as it revisits existing literature.
The paper explores the connections between Kriging (and Gaussian process regression) and neural networks, aiming to enhance machine learning techniques by making them more interpretable, reliable, and spatially aware through this combined perspective.
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.