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Fully Bayesian Unfolding
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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.
Forward citations
Cited by 2 Pith papers
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Neural Posterior Unfolding
A normalizing-flow-based Bayesian unfolding method (NPU) plus a modern Python implementation of Fully Bayesian Unfolding (FBU) are introduced and validated on Gaussian and simulated LHC jet data.
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Empirical-Bayes Unfolding of $\gamma$-ray Spectra
Empirical-Bayes hierarchical unfolding for gamma-ray spectra with Poisson ON/OFF likelihood, adaptive Richardson-Lucy prior, and NUTS posterior sampling, yielding spectra consistent with frequentist regularized ML.
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