Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses
Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses
复制标题
DOI：
10.1111/oik.05985
复制
发表时间：
2019-07-01
期刊：
影响因子：
3.4
通讯作者：
Lemoine, Nathan P.
中科院分区：
文献类型：
--
作者：
Lemoine, Nathan P.
在过去的二十年里，贝叶斯统计方法在生态学和进化中得到了广泛应用。许多先前的参考文献建立了实现贝叶斯方法的哲学和计算指南。然而，纳入先验信息的协议，贝叶斯哲学的定义特征，在生态文献中几乎不存在。在这里，我希望通过提供弱信息先验的“消费者指南”来鼓励在生态学和进化中使用弱信息先验。第一部分概述了生态学家应该放弃非信息性先验的三个原因：1)普通的平坦先验并不总是非信息性的；2)非信息性先验提供与更简单的频率论方法相同的结果；3)非信息性先验与频率论方法一样具有高I型和M型错误率。第二部分提供了实现信息先验的指南，其中我详细介绍了常用统计模型（即回归，方差分析，分层模型）的方便“参考”先验分布。然后，我使用模拟来直观地演示信息先验如何影响后验参数估计。有了这里提供的指导方针，我希望鼓励在生态学中使用弱信息先验进行贝叶斯分析。生态学家可以也应该讨论先验信息的适当形式，但应该考虑弱信息先验作为任何贝叶斯模型的新“默认”先验。
Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a 'consumer's guide' to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient 'reference' prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new 'default' prior for any Bayesian model.