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 Publication Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year A binned likelihood for stochastic models. Zbl 1416.62698Argüelles, C. A.; Schneider, A.; Yuan, T. 2019 Cited by 2 Authors 1 Argüelles, Carlos A. 1 Yuan, Tianlu Cited in 1 Serial 1 Journal of High Energy Physics Cited in 3 Fields 1 Statistics (62-XX) 1 Numerical analysis (65-XX) 1 Astronomy and astrophysics (85-XX) Citations by Year