  Determinantal points processes are a promising but relatively under-developed
tool in machine learning and statistical modelling, being the canonical
statistical example of distributions with repulsion. While their mathematical
formulation is elegant and appealing, their practical use, such as simply
sampling from them, is far from straightforward.Recent work has shown how a
particular type of determinantal point process defined on the compact
multidimensional space $[-1, 1]^d$ can be practically sampled and further shown
how such samples can be used to improve Monte Carlo integration.This work
extends those results to a new determinantal point process on $\mathbb{R}^d$ by
constructing a novel sampling scheme. Samples from this new process are shown
to be useful in Monte Carlo integration against Gaussian measure, which is
particularly relevant in machine learning applications.
