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MCLLH

swMATH ID: 34702
Software Authors: Argüelles, C. A.; Schneider, A.; Yuan, T.
Description: A binned likelihood for stochastic models. 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.
Homepage: https://link.springer.com/article/10.1007%2FJHEP06%282019%29030
Source Code:  https://github.com/austinschneider/MCLLH
Dependencies: C++; python
Keywords: event-by-event fluctuation; neutrino detectors and telescopes (experiments); unfolding
Related Software: HistFactory; GitHub; emcee
Cited in: 1 Document

Standard Articles

1 Publication describing the Software, including 1 Publication in zbMATH Year
A binned likelihood for stochastic models. Zbl 1416.62698
Argüelles, C. A.; Schneider, A.; Yuan, T.
2019

Citations by Year