Fully Bayesian Unfolding

2012/01/22 by G. Choudalakis, Choudalakis, Georgios · 4 citations
Chemistry · Computer Science · Mathematics · #Blind Source Separation Techniques #Data Analysis #FOS: Physical sciences #Spectroscopy and Chemometric Analyses #Statistical and numerical algorithms #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.1201.4612

openalex publication_date 2012/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• How to Use Experimental Data to Compute the Probability of Your Theory
• Improved iterative Bayesian unfolding
• On hypothesis testing, trials factor, hypertests and the BumpHunter
• Probably a discovery: Bad mathematics means rough scientific communication

Cited by

• Neural Posterior Unfolding
• Top-antitop charge asymmetry measurements in the dilepton channel with\n the ATLAS detector
• Kinematics of Top Quark Final States: A Snowmass White Paper
• Empirical Bayes unfolding of elementary particle spectra at the Large\n Hadron Collider

Related

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