Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution
Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution
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DOI：
10.1101/2020.12.10.419713
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发表时间：
2020-12
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影响因子：
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通讯作者：
Jeff S. Wesner;J. Pomeranz
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文献类型：
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作者：
Jeff S. Wesner;J. Pomeranz
贝叶斯数据分析在生态学中的应用越来越多，但先前的规范仍然侧重于选择非信息性的先验(例如，平坦或模糊的先验)。选择更多信息先验的一个障碍是，必须在模型参数(例如截距、斜率、西格玛)上指定先验，但先验知识通常存在于响应变量的水平上。对于生态学中常见的模型尤其如此，比如广义线性混合模型，它可能有一个链接函数和几十个参数，每个参数都需要一个先验分布。我们认为，这一困难可以通过从先前的预测分布进行模拟并在响应变量的尺度上可视化结果来克服。在这样做的过程中，可以很容易地看到一些关于参数的非信息性先验的常见选择，从而产生生物上不可能的反应变量的值。如果没有可视化，先前选择的这种影响是很难预见的。我们通过两个生态例子(捕食者-猎物的身体大小和蜘蛛对食物竞争的反应)使用模拟和可视化来演示优先选择的工作流程。这种方法并不新鲜，但生态学家采用这种方法将有助于更好地将先验信息纳入生态模型，从而最大限度地发挥贝叶斯数据分析的好处之一。
Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non-informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, sigmas), but prior knowledge often exists on the level of the response variable. This is particularly true for common models in ecology, like generalized linear mixed models, which may have a link function and dozens of parameters, each of which needs a prior distribution. We suggest that this difficulty can be overcome by simulating from the prior predictive distribution and visualizing the results on the scale of the response variable. In doing so, some common choices for non-informative priors on parameters can easily be seen to produce biologically impossible values of response variables. Such implications of prior choices are difficult to foresee without visualization. We demonstrate a workflow for prior selection using simulation and visualization with two ecological examples (predator-prey body sizes and spider responses to food competition). This approach is not new, but its adoption by ecologists will help to better incorporate prior information in ecological models, thereby maximizing one of the benefits of Bayesian data analysis.