This work falls within the context of predicting the value of a real function
at some input locations given a limited number of observations of this
function. The Kriging interpolation technique (or Gaussian process regression)
is often considered to tackle such a problem but the method suffers from its
computational burden when the number of observation points is large. We
introduce in this article nested Kriging predictors which are constructed by
aggregating sub-models based on subsets of observation points. This approach is
proven to have better theoretical properties than other aggregation methods
that can be found in the literature. Contrarily to some other methods it can be
shown that the proposed aggregation method is consistent. Finally, the
practical interest of the proposed method is illustrated on simulated datasets
and on an industrial test case with $10^4$ observations in a 6-dimensional
space.

本文介绍了一种基于 Kriging 插值技术的嵌套预测器方法，通过聚合基于观测点子集的子模型来减轻计算负担，该方法在理论上具有比其他文献中的聚合方法更好的性能，并在模拟数据集和工业测试案例中进行了实际验证。