Separation and Fitting of High-Dimensional Gaussians

We answer the question of how high-dimensional datasets, originating from a superposition of several Gaussian distributions, can be separated (or disentangled) again. Indeed, high dimensionality plays into our hands here, and we formalize this in the form of an asymptotic separation theorem. We also discuss parameter estimation (fitting) for a single Gaussian, using the maximum likelihood method.