A binned likelihood for stochastic models

被引：38

作者：

Arguelles, C. A. ^{[1
]}

Schneider, A. ^{[2
,3
]}

Yuan, T. ^{[2
,3
]}

机构：

[1] MIT, Dept Phys, Cambridge, MA 02139 USA

[2] Univ Wisconsin, Dept Phys, 1150 Univ Ave, Madison, WI 53706 USA

[3] Univ Wisconsin, Wisconsin IceCube Particle Astrophys Ctr, Madison, WI 53706 USA

来源：

JOURNAL OF HIGH ENERGY PHYSICS | 2019年 / 2019卷 / 06期

基金：

美国国家科学基金会;

关键词：

Event-by-event fluctuation; Neutrino Detectors and Telescopes (experiments); Unfolding;

D O I：

10.1007/JHEP06(2019)030

中图分类号：

O412 [相对论、场论]; O572.2 [粒子物理学];

学科分类号：

摘要：

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.

引用

页数：18

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