  We propose a practical and scalable Gaussian process model for large-scale
nonlinear probabilistic regression. Our mixture-of-experts model is
conceptually simple and hierarchically recombines computations for an overall
approximation of a full Gaussian process. Closed-form and distributed
computations allow for efficient and massive parallelisation while keeping the
memory consumption small. Given sufficient computing resources, our model can
handle arbitrarily large data sets, without explicit sparse approximations. We
provide strong experimental evidence that our model can be applied to large
data sets of sizes far beyond millions. Hence, our model has the potential to
lay the foundation for general large-scale Gaussian process research.
