Improved iterative Bayesian unfolding

Abstract

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.

31 pages, 7 figures, presented at the Alliance Workshop on Unfolding and Data Correction (Hamburg, Germany, 27-28 May 2010). Slides of the presentation as well as the R code can be found in http://www.roma1.infn.it/~dagos/prob+stat.html#unf2

References in corpus (1)

• Fits, and especially linear fits, with errors on both axes, extra variance of the data points and other complications

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