  This paper reviews the basic ideas behind a Bayesian unfolding published some
years ago and improves their implementation. In particular, uncertainties are
now treated at all levels by probability density functions and their
propagation is performed by Monte Carlo integration. Thus, small numbers are
better handled and the final uncertainty does not rely on the assumption of
normality. Theoretical and practical issues concerning the iterative use of the
algorithm are also discussed. The new program, implemented in the R language,
is freely available, together with sample scripts to play with toy models.
