论文标题
简单且统计上可靠的建议，用于分析物理理论
Simple and statistically sound recommendations for analysing physical theories
论文作者
论文摘要
取决于许多参数或针对来自许多不同实验的数据进行测试的物理理论对统计推断提出了独特的挑战。粒子物理，天体物理学和宇宙学中的许多模型属于这一类别。这些问题通常与统计上不合适的临时方法相互避免，涉及通过多个实验估计的参数间隔的相交以及模型参数的随机或网格采样。尽管这些方法易于应用，但即使在低维参数空间中也表现出病理，并且在更高维度上使用和解释很快就会成为问题。在本文中，我们为超越这些过程提供了明确的指导，建议在可能的情况下进行统计上声音推理的简单方法，以及可以帮助这样做的易于使用的软件工具和标准的建议。我们的目的是为缺乏全面统计培训的任何物理学家提供有关得出正确的科学结论的建议，而分析负担只会增加。我们的示例可以在https://doi.org/10.5281/zenodo.4322283上公开获得的代码复制。
Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at https://doi.org/10.5281/zenodo.4322283.
