Gaussian process regression models are widely used in modern statistics and machine learning due to their flexibility, interpretability, built-in uncertainty quantification, and strong theoretical foundations. However, training Gaussian processes (GPs) has a computational cost of $O(n^3)$, while the memory and prediction costs are also $O(n^2)$. For large datasets commonly encountered in practice, this becomes infeasible and necessitates the use of approximate posterior methods. In this work, we propose a class of approximations based on sparse covariance matrices, where the kernel matrix is sparsified according to the spatial proximity of the design points. We analyze the accuracy of the resulting approximate posterior distribution and show that the proposed approach preserves optimal statistical inference properties, provided that the dependencies among a sufficient number of neighboring points for each feature are retained. We derive explicit sufficient and necessary conditions on the number of neighbors required in a general setting and apply these results to standard kernels, including the squared exponential and Matérn kernels. Our theoretical findings are supported by numerical experiments.

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