Department of Chemistry, Stanford University, Stanford, California 94305, USA.
J Phys Chem B. 2010 Jan 14;114(1):280-92. doi: 10.1021/jp906786b.
Single molecule spectroscopy experiments and molecular dynamics simulations have several profound features in common, chief among which is that both follow the dynamics of some degrees of freedom of a single molecule over time. The analysis is essentially the same: one investigates the changes in the degrees of freedom followed. For instance, in a single molecule fluorescence experiment, the degree of freedom is often the number of photons detected in some time period. In this article, we introduce a straightforward Bayesian method for detecting if and when changes occurred. In contrast to methods based upon maximum likelihood estimates, a Bayesian approach allows for a more systematic means not only to change point detection but also to cluster the data into states. Most importantly, the Bayesian method supplies a simpler hypothesis testing framework. Although we focus on Poisson-distributed data, the Bayesian methods outlined here can in principle be applied to data sampled from any distribution.
单分子光谱实验和分子动力学模拟有几个深刻的共同特征，其中最重要的是，两者都随时间跟踪单个分子的某些自由度的动态变化。分析本质上是相同的：研究所跟踪的自由度的变化。例如，在单分子荧光实验中，自由度通常是在某个时间段内检测到的光子数。在本文中，我们介绍了一种用于检测是否发生以及何时发生变化的简单贝叶斯方法。与基于最大似然估计的方法相比，贝叶斯方法不仅允许更系统地进行变化点检测，还允许将数据聚类为状态。最重要的是，贝叶斯方法提供了一个更简单的假设检验框架。虽然我们专注于泊松分布数据，但这里概述的贝叶斯方法原则上可以应用于从任何分布中采样的数据。
