  Bayesian inference is applied directly to the problem of unfolding. The
outcome is a posterior probability density for the spectrum before smearing,
defined in the multi-dimensional space of all possible spectra. Regularization
consists in choosing a non-constant prior. Despite some similarity, the fully
bayesian unfolding (FBU) method, presented here, should not be confused with
D'Agostini's iterative method.
