A binned likelihood for stochastic models
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13130_2019_Article_10708.pdf
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a5d101e7b9e6adc8167b266edd3e9988
Author(s)
Argüelles, C.A.
Schneider, A.
Yuan, T.
Date Issued
June 10, 2019
Publisher
Springer Berlin Heidelberg
Citation
Journal of High Energy Physics. 2019 Jun 10;2019(6):30
Version
Final published version
Abstract
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/JHEP06(2019)030
