Hines Keegan E
Department of Neuroscience, University of Texas at Austin, Austin, Texas.
Biophys J. 2015 May 5;108(9):2103-13. doi: 10.1016/j.bpj.2015.03.042.
Bayesian inference is a powerful statistical paradigm that has gained popularity in many fields of science, but adoption has been somewhat slower in biophysics. Here, I provide an accessible tutorial on the use of Bayesian methods by focusing on example applications that will be familiar to biophysicists. I first discuss the goals of Bayesian inference and show simple examples of posterior inference using conjugate priors. I then describe Markov chain Monte Carlo sampling and, in particular, discuss Gibbs sampling and Metropolis random walk algorithms with reference to detailed examples. These Bayesian methods (with the aid of Markov chain Monte Carlo sampling) provide a generalizable way of rigorously addressing parameter inference and identifiability for arbitrarily complicated models.
贝叶斯推理是一种强大的统计范式，在许多科学领域都颇受欢迎，但在生物物理学中的应用速度稍慢。在这里，我通过聚焦生物物理学家熟悉的示例应用，提供一个易于理解的贝叶斯方法使用教程。我首先讨论贝叶斯推理的目标，并展示使用共轭先验进行后验推理的简单示例。然后我描述马尔可夫链蒙特卡罗采样，特别是结合详细示例讨论吉布斯采样和 metropolis 随机游走算法。这些贝叶斯方法（借助马尔可夫链蒙特卡罗采样）提供了一种通用的方法，可严格解决任意复杂模型的参数推断和可识别性问题。
