拟高斯缺失高阶不确定性
Quasi-Gaussian Missing Higher Order Uncertainties
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中文总结 AI 辅助
本文回顾了估计微扰预测中缺失高阶不确定性的贝叶斯模型，通过修改似然和先验，获得了拟高斯形状的光滑概率分布。
中文摘要 AI 辅助
我们回顾了用于估计微扰预测中缺失高阶所引发不确定性的贝叶斯模型。这些模型返回的概率分布具有特殊形状，通常与物理分析中常假设的高斯分布大不相同。在本工作中，通过适当修改模型的似然函数和先验，我们能够获得具有拟高斯形状的光滑概率分布。
英文摘要
We review Bayesian models for estimating the uncertainty induced by missing higher orders in perturbative predictions. These models return probability distributions, which have peculiar shapes typically very different from the Gaussian distribution often assumed in physics analysis. In this work, we are able to obtain smooth probability distributions with quasi-Gaussians shapes by suitably modifying the likelihood and the priors of the models.
