  The Atacama Large Millimeter/submillimeter Array (ALMA) is currently
revolutionizing observational astrophysics. The aperture synthesis technique
provides angular resolution otherwise unachievable with the conventional
single-aperture telescope. However, recovering the image from the inherently
undersampled data is a challenging task. The CLEAN algorithm has proven
successful and reliable and is commonly used in imaging the interferometric
observations. It is not, however, free of limitations. Point-source assumption,
central to the CLEAN is not optimal for the extended structures of molecular
gas recovered by ALMA. Additionally, negative fluxes recovered with CLEAN are
not physical. This begs to search for alternatives that would be better suited
for specific science cases. We present the recent developments in imaging ALMA
data using Bayesian inference techniques, namely the RESOLVE algorithm This
algorithm, based on information field theory \cite{Ensslin2013}, has been
already successfully applied to image the Very Large Array data. We compare the
capability of both CLEAN and RESOLVE to recover known sky signal, convoluted
with the simulator of ALMA observation data and we investigate the problem with
a set of actual ALMA observations.
