Improved iterative Bayesian unfolding

2010/10/04 by G. D’Agostini, D'Agostini, G. · 8 citations
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Data Analysis #FOS: Physical sciences #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.1010.0632

openalex publication_date 2010/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Neural network-based deconvolution for GeV-Scale Gamma-Ray Spectroscopy
• Improved error correction with leakage reduction units built into qubit measurement in a superconducting quantum processor
• Measurement of the average transverse momentum of forward prompt charged particles in pp and pPb collisions at √s_NN = 5.02 TeV
• Unbinned measurement of thrust in e⁺e^- collisions at √(s) = 91.2 GeV with ALEPH archived data
• Iterative HOMER with uncertainties
• Analysis note: measurement of thrust in e⁺e^- collisions at √(s) = 91 GeV with archived ALEPH data
• Unfolding by Folding: a resampling approach to the problem of matrix inversion without actually inverting any matrix
• High-Dimensional Unfolding in Large Backgrounds

Related

• No-prior Bayesian inference reIMagined: probabilistic approximations of inferential models
• Bayesian Essentials with R: The Complete Solution Manual
• Probability measure changes in Monte Carlo simulation
• Hug and Hop: a discrete-time, non-reversible Markov chain Monte-Carlo algorithm
• Bayesian Computation with Intractable Likelihoods