A binned likelihood for stochastic models

  • C. A. Argüelles
  • A. Schneider
  • T. YuanEmail author
Open Access
Regular Article - Experimental Physics


Metrics of model goodness-of-fit, model comparison, and model parameter estimation are the main categories of statistical problems in science. Bayesian and frequentist methods that address these questions often rely on a likelihood function, which is the key ingredient in order to assess the plausibility of model parameters given observed data. In some complex systems or experimental setups, predicting the outcome of a model cannot be done analytically, and Monte Carlo techniques are used. In this paper, we present a new analytic likelihood that takes into account Monte Carlo uncertainties, appropriate for use in the large and small sample size limits. Our formulation performs better than semi-analytic methods, prevents strong claims on biased statements, and provides improved coverage properties compared to available methods.


Event-by-event fluctuation Neutrino Detectors and Telescopes (experiments) Unfolding 


Open Access

This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.


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Copyright information

© The Author(s) 2019

Authors and Affiliations

  1. 1.Department of PhysicsMassachusetts Institute of TechnologyCambridgeU.S.A.
  2. 2.Department of Physics and Wisconsin IceCube Particle Astrophysics CenterUniversity of WisconsinMadisonU.S.A.

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