Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo data

@article{Glsenkamp2018ProbabilisticTO,
  title={Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo data},
  author={T. Gl{\"u}senkamp},
  journal={The European Physical Journal Plus},
  year={2018},
  volume={133},
  pages={1-22}
}
  • T. Glüsenkamp
  • Published 2018
  • Physics
  • The European Physical Journal Plus
  • Abstract.Parameter estimation in HEP experiments often involves Monte Carlo simulation to model the experimental response function. A typical application are forward-folding likelihood analyses with re-weighting, or time-consuming minimization schemes with a new simulation set for each parameter value. Problematically, the finite size of such Monte Carlo samples carries intrinsic uncertainty that can lead to a substantial bias in parameter estimation if it is neglected and the sample size is… CONTINUE READING
    A binned likelihood for stochastic models
    • 7
    • Highly Influenced
    • PDF
    A unified perspective on modified Poisson likelihoods for limited Monte Carlo data
    • 1
    A unified perspective on modified Poisson likelihoods for limited Monte Carlo data

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 33 REFERENCES
    Fitting using finite Monte Carlo samples
    • 391
    An invariant form for the prior probability in estimation problems
    • 1,808
    • PDF
    Discrete Multivariate Distributions
    • 634
    • PDF
    Multiple Hypergeometric Functions: Probabilistic Interpretations and Statistical Uses
    • 86
    Searches for Extended and Point-like Neutrino Sources with Four Years of IceCube Data
    • 165
    • PDF
    The distribution of the sum of independent gamma random variables
    • 415
    Astroparticle physics with high energy neutrinos: from AMANDA to IceCube
    • 65
    • PDF
    Bayes and Frequentism: a particle physicist’s perspective
    • 20
    • PDF
    Statistics of weighted Poisson events and its applications
    • 14
    • Highly Influential
    • PDF