Biophysics experiments performed at single-molecule resolution contain exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. Here, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous manner to incorporate information from multiple experiments into a single analysis and to find the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.

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贝叶斯推断：分析单分子实验的综合方法

以单分子分辨率进行的生物物理实验包含对生物系统的结构细节和动态行为的出色洞察力。但是，明确地从相应的实验数据中提取此信息需要应用生物物理模型。在这里，我们讨论如何使用概率论将这些模型应用于单分子数据。许多当前的单分子数据分析方法有时甚至在不知不觉中应用了概率论的某些部分，因此错过了此自洽框架提供的全部好处。概率论的完整应用涉及一个称为贝叶斯推理的过程，该过程充分考虑了单分子实验固有的不确定性。另外，使用贝叶斯推论提供了一种科学严谨的方式，可以将来自多个实验的信息整合到单个分析中，并为实验找到最佳的生物物理模型，而不会过度拟合数据。这些优点使贝叶斯方法成为分析任何类型的单分子实验的理想选择。