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arXiv · 1901.04645

A binned likelihood for stochastic models

Carlos A. Argüelles·Austin Schneider·Tianlu Yuan
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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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Carlos A. Argüelles, Austin Schneider, Tianlu Yuan. 2019-01-15. A binned likelihood for stochastic models. https://doi.org/10.1007/jhep06(2019)030

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