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2019

A binned likelihood for stochastic models

Argüelles, Carlos A., Schneider, Austin, Yuan, Tianlu

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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.

A binned likelihood for stochastic models · Around

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Then

G. Bohm and G. Zech, Statistics of weighted Poisson events and its applications , Nucl. Instrum. Meth. A748
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T. Glüsenkamp, Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo data , Eur. Phys. J. Plus 133
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C. Argüelles, A. Schneider and T. Yuan, “ MCLLH
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