  We introduce (H)DPGMM, a hierarchical Bayesian non-parametric method based on
the Dirichlet Process Gaussian Mixture Model, designed to infer data-driven
population properties of astrophysical objects without being committal to any
specific physical model. We investigate the efficacy of our model on simulated
datasets and demonstrate its capability to reconstruct correctly a variety of
population models without the need of fine-tuning of the algorithm. We apply
our method to the problem of inferring the black hole mass function given a set
of gravitational wave observations from LIGO and Virgo, and find that the
(H)DPGMM infers a binary black hole mass function that is consistent with
previous estimates without the requirement of a theoretically motivated
parametric model. Although the number of systems observed is still too small
for a robust inference, (H)DPGMM confirms the presence of at least two distinct
modes in the observed merging black holes mass function, hence suggesting in a
model-independent fashion the presence of at least two classes of binary black
hole systems.
