Improved iterative Bayesian unfolding

TL;DR

This paper enhances Bayesian unfolding by incorporating comprehensive uncertainty treatment via probability density functions and Monte Carlo methods, improving accuracy especially with small data sets, and provides an accessible R implementation.

Contribution

It introduces an improved Bayesian unfolding method with full uncertainty propagation and practical implementation in R, addressing limitations of previous approaches.

Findings

Better handling of small data sets.

Uncertainty estimation no longer assumes normality.

Open-source R implementation available.

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.