Fully Bayesian Unfolding
Data Analysis, Statistics and Probability
2012-06-01 v4
Abstract
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.
Cite
@article{arxiv.1201.4612,
title = {Fully Bayesian Unfolding},
author = {Georgios Choudalakis},
journal= {arXiv preprint arXiv:1201.4612},
year = {2012}
}
Comments
24 pages of text, 40 pages of figures