In most low-level computer vision problems, very little information (if any) is known about the true underlying probability density function, such as its shape, number of mixture components, etc.. Due to this lack of knowledge, parametric approaches are less relevant, rather one has to rely on non-parametric methods. In this note we consider the construction and convergence proof of the non-par...

Assume that a family of stochastic processes on some Polish space E converges to a deterministic process; the convergence is in distribution (hence in probability) at every fixed point in time. This assumption holds for a large family of processes, among which many mean field interaction models and is weaker than previously assumed. We show that any limit point of an invariant probability of th...