  Aims. We develop, test and characterise of a new statistical tool
(intelligent system) for the sifting and analysis of nearby young open cluster
(NYOC) populations. Methods. Using a Bayesian formalism, this statistical tool
is able to obtain the posterior distributions of parameters governing the
cluster model. It also uses hierarchical bayesian models to establish weakly
informative priors, and incorporates the treatment of missing values and
non-homogeneous (heteroscedastic) observational uncertainties. Results. From
simulations, we estimate that this statistical tool renders kinematic (proper
motion) and photometric (luminosity) distributions of the cluster population
with a contamination rate of $5.8 \pm 0.2$ %. The luminosity distributions and
present day mass function agree with the ones found by Bouy et al. (2015b) on
the completeness interval of the survey. At the probability threshold of
maximum accuracy, the classifier recovers $\sim$ 90% of Bouy et al. (2015b)
candidate members and finds 10% of new ones. Conclusions. A new statistical
tool for the analysis of NYOC is introduced, tested and characterised. Its
comprehensive modelling of the data properties allows it to get rid of the
biases present in previous works. In particular, those resulting from the use
of only completely observed (non-missing) data and the assumption of
homoskedastic uncertainties. Also, its Bayesian framework allows it to properly
propagate observational uncertainties into membership probabilities and cluster
velocity and luminosity distributions. Our results are in a general agreement
with those from the literature, although we provide the most up-to-date and
extended list of candidate members of the Pleiades cluster.
