  Persistent homology probes topological properties from point clouds and
functions. By looking at multiple scales simultaneously, one can record the
births and deaths of topological features as the scale varies. In this paper we
use a statistical technique, the empirical bootstrap, to separate topological
signal from topological noise. In particular, we derive confidence sets for
persistence diagrams and confidence bands for persistence landscapes.
