A binned likelihood for stochastic models
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Publication:2314898
DOI10.1007/JHEP06(2019)030zbMATH Open1416.62698arXiv1901.04645OpenAlexW3101481265WikidataQ127733421 ScholiaQ127733421MaRDI QIDQ2314898
Author name not available (Why is that?)
Publication date: 30 July 2019
Published in: (Search for Journal in Brave)
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.
Full work available at URL: https://arxiv.org/abs/1901.04645
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