choices of parameters and representation space. Finally, in Section \[sect:lim-concl\] we expose the weaknesses and limitations of this methodology and point out future developments that circumvent these limitations.

Membership probability estimation {#sect:rml}
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Observations
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We explore probabilistic models for the estimation of membership probabilities in a multidimensional data set composed of proper motions and apparent magnitudes of sources in the Pleiades cluster sky region. The origin of the data set and the typical uncertainties are described in [@dance1]. The data set is composed of proper motions in equatorial coordinates ($\mu_{\alpha}, \mu_{\delta}$) and measurements in the u, g, r, i, Z, Y, J, H, and K bands for 3577478 sources. From these, we selected 2066111 sources with proper motions between $\pm$ 100 mas yr$^{-1}$ (hereafter, the DANCe data set). Unfortunately, the data set is inhomogeneous in the various bands, with different spatial coverages, completenesses, and limiting magnitudes [see Fig. 1 in @dance1 for a graphical representation of the complex spatial coverage of the DANCe Pleaides data set].

As a consequence, only 132067 sources are complete (have photometric measurements in all bands). Table \[nrofmeas\] summarises the number of missing measurements in each variable, and Fig. \[data\] shows the distribution of sources 