  This work presents a fast and scalable algorithm for incremental learning of
Gaussian mixture models. By performing rank-one updates on its precision
matrices and determinants, its asymptotic time complexity is of \BigO{NKD^2}
for $N$ data points, $K$ Gaussian components and $D$ dimensions. The resulting
algorithm can be applied to high dimensional tasks, and this is confirmed by
applying it to the classification datasets MNIST and CIFAR-10. Additionally, in
order to show the algorithm's applicability to function approximation and
control tasks, it is applied to three reinforcement learning tasks and its
data-efficiency is evaluated.
