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首页> 外文期刊>Conservation Biology >The use of Bayesian model averaging to better represent uncertainty in ecological models
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The use of Bayesian model averaging to better represent uncertainty in ecological models

机译：使用贝叶斯模型平均可以更好地表示生态模型中的不确定性

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In conservation biology, uncertainty about the choice of a statistical model is rarely considered. Model-selection uncertainty occurs whenever one model is chosen over plausible alternative models to represent understanding about a process and to make predictions about future observations. The standard approach to representing prediction uncertainty involves the calculation of prediction (or confidence) intervals that incorporate uncertainty about parameter estimates contingent on the choice of a "best" model chosen to represent truth. However, this approach to prediction based on statistical models tends to ignore model-selection uncertainty, resulting in overconfident predictions. Bayesian model averaging (BMA) has been promoted in a range of disciplines as a simple means of incorporating model-selection uncertainty into statistical inference and prediction. Bayesian model averaging also provides a formal framework for incorporating prior knowledge about the process being modeled. We provide an example of the application of BMA in modeling and predicting the spatial distribution of an arboreal marsupial in the Eden region of southeastern Australia. Other approaches to estimating prediction uncertainty are discussed. [References: 61]
机译：在保护生物学中，很少考虑关于统计模型选择的不确定性。每当在合理的替代模型中选择一个模型来代表对过程的理解并做出有关未来观察的预测时，就会发生模型选择的不确定性。表示预测不确定性的标准方法涉及预测（或置信）区间的计算，该区间包含有关参数估计的不确定性，具体取决于选择用来代表真相的“最佳”模型的选择。但是，这种基于统计模型的预测方法倾向于忽略模型选择的不确定性，从而导致预测结果过于自信。贝叶斯模型平均（BMA）已作为一系列将模型选择不确定性纳入统计推断和预测的简单方法而得到了推广。贝叶斯模型平均还为合并有关建模过程的先验知识提供了一个正式的框架。我们提供了BMA在建模和预测澳大利亚东南部伊甸园地区有树有袋动物的空间分布方面的应用示例。讨论了估计预测不确定性的其他方法。 [参考：61]

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