An iterative technique is described for generating estimates to the solutions of rectification and deconvolution problems in statistical astronomy. The technique, which derives from Bayes' theorem on conditional probabili- ties, conserves the constraints on frequency distributions (i.e., normalization and non-negativeness) and, at each iteration, increases the likelihood of the observed sample. The behavior of the technique is explored by applying it to roblems whose solutions are known in the limit of infinite sample size, and excellent results are obtained after a few iterations. The astronomical use of the technique is illustrated by applying it to the problem of rectifying distributions of v sin i for aspect effect; calculations are also reported illustrating the technique's possible use for correcting radio-astronomical observations for beam-smoothing. Application to the problem of obtaining unbiased, smoothed histograms is also suggested.