Abstract
An iterative technique is described for generating estimates to the solutions of rectification and deconvolution in statistical astronomy. The technique, which derives from Bayes’ Theorem on conditional probabilities, 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 problems 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.
Author
Lucy, L. B.
Journal
Astronomical Journal
Paper Publication Date
June 1974
Paper Type
Astrostatistics
Submitter’s Remarks
EM Algorithm