The Use of Bayesian Model Averaging to Better Represent Uncertainty in Ecological Models
The Use of Bayesian Model Averaging to Better Represent Uncertainty in Ecological Models
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DOI：
10.1111/j.1523-1739.2003.00614.x
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发表时间：
2003-12
影响因子：
6.3
通讯作者：
Brendan A. Wintle;M. McCarthy;C. Volinsky;R. Kavanagh
中科院分区：
文献类型：
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作者：
Brendan A. Wintle;M. McCarthy;C. Volinsky;R. Kavanagh
翻译后摘要：在保护生物学中，很少考虑统计模型的选择的不确定性。模型选择不确定性发生在当一个模型被选择而不是合理的替代模型时，以代表对过程的理解并对未来的观察做出预测。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，该区间包含参数估计值的不确定性，取决于选择代表真理的“最佳”模型。然而，这种基于统计模型的预测方法往往忽略了模型选择的不确定性，导致过度自信的预测。贝叶斯模型平均（Bayesian Model Averaging，BMA）已在一系列学科中推广，作为将模型选择不确定性纳入统计推断和预测的简单方法。贝叶斯模型平均还提供了一个正式的框架，用于合并有关建模过程的先验知识。我们提供了一个应用BMA在建模和预测的空间分布的树栖有袋动物在伊甸园地区的澳大利亚东南部的一个例子。其他方法来估计预测的不确定性进行了讨论。
Abstract: 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.