Bayesian model averaging: A tutorial
Bayesian model averaging: A tutorial
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DOI：
10.1214/ss/1009212519
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发表时间：
1999-11-01
影响因子：
5.7
通讯作者：
Volinsky, CT
中科院分区：
文献类型：
--
作者：
Hoeting, JA;Madigan, D;Volinsky, CT
标准的统计实践忽略了模型的不确定性。数据分析人员通常从一些模型类别中选择一个模型，然后就像所选模型生成了数据一样进行操作。这种方法忽略了模型选择中的不确定性，导致过度自信的推断和决策比人们认为的风险更大。贝叶斯模型平均（BMA）为计算这种模型的不确定性提供了一种连贯的机制。最近出现了几种实施BMA的方法。我们讨论了这些方法，并给出了一些例子。在这些示例中，BMA提供了改进的样本外预测性能。我们还提供了目前可用的BMA软件目录。
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples. In these examples, BMA provides improved out-of-sample predictive performance. We also provide a catalogue of currently available BMA software.