stellar kinematic groups, where the authors treat the astrometric and photometric data consistently in a probabilistic framework, albeit assuming that all measurements included in the inference of membership probabilities are independent and uncorrelated. In general, we find that the application of magnitude-dependent hard thresholds in consecutive CMDs has two main drawbacks: it renders the resulting membership probabilities inconsistent across magnitude bins, and produces membership probabilities only for sources with measurements in each and every CMD used for selection.

have recently developed an unsupervised method for computing membership probabilities from celestial coordinates and photometric measurements using principal components and clustering methods. Their methodology is related to the one presented in this work, and is as well capable of incorporating models of uncertainty and incomplete observations. As the authors point out, one of the main disadvantages of their method is the linear projection implicit to the principal components analysis. The distribution of sources in the colour-magnitude space is non-linear in general, and a linear projection inevitably results in membership probabilities that are systematically biased .

A final remark on the classical methodology for the determination of membership probabilities in stellar clusters or groups is that the aforecited works do not incorporate measurement 