  Bayesian component separation techniques have played a central role in the
data reduction process of Planck. The most important strength of this approach
is its global nature, in which a parametric and physical model is fitted to the
data. Such physical modeling allows the user to constrain very general data
models, and jointly probe cosmological, astrophysical and instrumental
parameters. This approach also supports statistically robust goodness-of-fit
tests in terms of data-minus-model residual maps, which are essential for
identifying residual systematic effects in the data. The main challenges are
high code complexity and computational cost. Whether or not these costs are
justified for a given experiment depends on its final uncertainty budget. We
therefore predict that the importance of Bayesian component separation
techniques is likely to increase with time for intensity mapping experiments,
similar to what has happened in the CMB field, as observational techniques
mature, and their overall sensitivity improves.
