贝叶斯与频率论：粒子物理学家的观点
Bayes and Frequentism: a Particle Physicist's perspective
摘要：
在几乎每一个科学领域，实验都包括收集数据，然后分析数据。分析阶段通常包括试图提取一些物理参数并估计其不确定性;这被称为参数确定。一个例子是从高能质子-质子碰撞收集的数据中确定顶夸克的质量。另一个不同的目的是在两种可能的假设之间做出选择。例如，关于遥远星系的衰退速度s的数据是否与它们的距离d成正比，或者它们是否更适合宇宙加速膨胀的模型？这种统计分析有两种基本方法-贝叶斯和频率论。本文讨论了他们在概率方法上的不同，然后继续考虑这如何影响他们处理参数确定和假设检验的方式。这些例子取自日常生活和粒子物理学。
英文摘要：
In almost every scientific field, an experiment involves collecting data and then analysing it. The analysis stage will often consist in trying to extract some physical parameter and estimating its uncertainty; this is known as Parameter Determination. An example would be the determination of the mass of the top quark, from data collected from high energy proton-proton collisions. A different aim is to choose between two possible hypotheses. For example, are data on the recession speed s of distant galaxies proportional to their distance d, or do they fit better to a model where the expansion of the Universe is accelerating? There are two fundamental approaches to such statistical analyses - Bayesian and Frequentist. This article discusses the way they differ in their approach to probability, and then goes on to consider how this affects the way they deal with Parameter Determination and Hypothesis Testing. The examples are taken from every-day life and from Particle Physics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
