  Persistent homology provides a robust methodology to infer topological
structures from point cloud data. Here we explore the persistent homology of
point clouds embedded into a probabilistic setting, exploiting the theory of
point processes. We introduce measures on the space of persistence diagrams and
the self-similar scaling of a one-parameter family of these. As the main result
we prove a packing relation between the occurring scaling exponents.
