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A critical study of the Monte Carlo replica method

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arxiv 2404.10056 v1 pith:TCSGCQ6N submitted 2024-04-15 hep-ph hep-ex

A critical study of the Monte Carlo replica method

classification hep-ph hep-ex
keywords methodbayesiancarlofitsmontereplicacoefficientscontext
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present a detailed mathematical study of the Monte Carlo replica method as applied in the global fitting literature from the high-energy physics theory community. For the first time, we provide a rigorous derivation of the parameter distributions implied by the method, and show that, whilst they agree with Bayesian posteriors for linear models, they disagree otherwise. We proceed to numerically quantify the disagreement between the Monte Carlo replica method and the Bayesian method in the context of two phenomenologically relevant scenarios: fits of the SMEFT Wilson coefficients, and fits of PDFs (albeit in a toy scenario). In both scenarios, we find that uncertainty estimates of the quantities of interest are discrepant between the two approaches when non-linearity is relevant. Our findings motivate future investigation of Bayesian methodologies for global PDF fits, especially in the context of simultaneous determination of PDFs and SMEFT Wilson coefficients.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Propagating data noise through the fit: the Monte Carlo replica distribution

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    Derives that the MC replica method produces a distribution differing from the Bayesian Laplace approximation by a single computable matrix (residual-weighted Hessian), whose sign and magnitude determine over- or under...

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    Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.

  3. Hyperoptimisation algorithm for the next generation of PDF determinations: ensemble regression with an unbiased selection model

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