  Data from radio interferometers provide a substantial challenge for
statisticians. It is incomplete, noise-dominated and originates from a
non-trivial measurement process. The signal is not only corrupted by imperfect
measurement devices but also from effects like fluctuations in the ionosphere
that act as a distortion screen. In this paper we focus on the imaging part of
data reduction in radio astronomy and present RESOLVE, a Bayesian imaging
algorithm for radio interferometry in its new incarnation. It is formulated in
the language of information field theory. Solely by algorithmic advances the
inference could be sped up significantly and behaves noticeably more stable
now. This is one more step towards a fully user-friendly version of RESOLVE
which can be applied routinely by astronomers.
