  The increased demand for online prediction and the growing availability of
large data sets drives the need for computationally efficient models. While
exact Gaussian process regression shows various favorable theoretical
properties (uncertainty estimate, unlimited expressive power), the poor scaling
with respect to the training set size prohibits its application in big data
regimes in real-time. Therefore, this paper proposes dividing local Gaussian
processes, which are a novel, computationally efficient modeling approach based
on Gaussian process regression. Due to an iterative, data-driven division of
the input space, they achieve a sublinear computational complexity in the total
number of training points in practice, while providing excellent predictive
distributions. A numerical evaluation on real-world data sets shows their
advantages over other state-of-the-art methods in terms of accuracy as well as
prediction and update speed.
