  Coalescing compact binaries emitting gravitational wave (GW) signals, as
recently detected by the Advanced LIGO-Virgo network, constitute a population
over the multi-dimensional space of component masses and spins, redshift, and
other parameters. Characterizing this population is a major goal of GW
observations and may be approached via parametric models. We demonstrate
hierarchical inference for such models with a method that accounts for
uncertainties in each binary merger's individual parameters, for mass-dependent
selection effects, and also for the presence of a second population of
candidate events caused by detector noise. Thus, the method is robust to
potential biases from a contaminated sample and allows us to extract
information from events that have a relatively small probability of
astrophysical origin.
