Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK.
Neuroimage. 2012 Aug 15;62(2):801-10. doi: 10.1016/j.neuroimage.2011.10.047. Epub 2011 Oct 20.
Bayesian inference has taken FMRI methods research into areas that frequentist statistics have struggled to reach. In this article we will consider some of the early forays into Bayes and what motivated its use. We shall see the impact that Bayes has had on haemodynamic modelling, spatial modelling, group analysis, model selection and brain connectivity analysis; and consider how these advancements have spun-off into related areas of neuroscience and some of the challenges that remain. Bayes has brought to the table inference flexibility, incorporation of prior information, adaptive regularisation and model selection. But perhaps more important than these things, is the ability of Bayes to empower the methods researcher with a mathematically principled framework for inferring on any model.
贝叶斯推断将功能磁共振成像(fMRI)方法研究引入了 频率统计学难以企及的领域。在本文中,我们将探讨贝叶斯推断的早期探索以及其应用的动机。我们将看到贝叶斯推断对血流动力学建模、空间建模、组分析、模型选择和脑连接分析的影响,并考虑这些进展如何衍生到神经科学的相关领域以及仍然存在的一些挑战。贝叶斯推断为推理提供了灵活性、先验信息的纳入、自适应正则化和模型选择。但也许比这些更重要的是,贝叶斯推断能够为方法研究人员提供一个数学上有原则的框架,以便对任何模型进行推断。