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A binned likelihood for stochastic models
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13130_2019_Article_10708.pdf
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886.24 KB
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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.
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DOI of Published Version
https://doi.org/10.1007/JHEP06(2019)030
