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We introduce (H)DPGMM, a hierarchical Bayesian non-parametric method based onthe Dirichlet Process Gaussian Mixture Model, designed to infer data-drivenpopulation properties of astrophysical objects without being committal to anyspecific physical model. We investigate the efficacy of our model on simulateddatasets and demonstrate its capability to reconstruct correctly a variety ofpopulation models without the need of fine-tuning of the algorithm. We applyour method to the problem of inferring the black hole mass function given a setof gravitational wave observations from LIGO and Virgo, and find that the(H)DPGMM infers a binary black hole mass function that is consistent withprevious estimates without the requirement of a theoretically motivatedparametric model. Although the number of systems observed is still too smallfor a robust inference, (H)DPGMM confirms the presence of at least two distinctmodes in the observed merging black holes mass function, hence suggesting in amodel-independent fashion the presence of at least two classes of binary blackhole systems.

(H)DPGMM: A Hierarchy of Dirichlet Process Gaussian Mixture Models for the inference of the black hole mass function