Department of Physics, Graduate School of Science, Kyoto University, Kyoto, Japan.
J Chem Phys. 2011 Feb 28;134(8):085108. doi: 10.1063/1.3516587.
In single-molecule protein experiments, the observable variables are restricted within a small fraction of the entire degrees of freedom. Therefore, to investigate the physical nature of proteins in detail, we always need to estimate the hidden internal structure referring only to the accessible degrees of freedom. We formulate this problem on the basis of Bayesian inference, which can be applied to various complex systems. In the ideal case, we find that in general the framework actually works. Although careful numerical studies confirm that our method outperforms the conventional method by up to two orders of magnitude, we find a striking phenomenon: a loss-of-precision transition occurs abruptly when the design of the observation system is inappropriate. The basic features of the proposed method are illustrated using a simple but nontrivial model.
在单分子蛋白质实验中，可观察变量被限制在整个自由度的一小部分内。因此，为了详细研究蛋白质的物理性质，我们总是需要仅参照可及自由度来估计隐藏的内部结构。我们基于贝叶斯推断来构建这个问题，这一方法可应用于各种复杂系统。在理想情况下，我们发现该框架通常是有效的。虽然仔细的数值研究证实，我们的方法比传统方法的性能高出两个数量级，但我们发现了一个惊人的现象：当观察系统的设计不当时，精度损失会突然发生。我们使用一个简单但非平凡的模型来说明所提出方法的基本特征。
