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Multimodel Ranking and Inference in Ground Water Modeling

机译：地下水建模中的多模型排序和推断

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Uncertainty of hydrogeologic conditions makes it important to consider alternative plausible models in an effort to evaluate the character of a ground water system, maintain parsimony, and make predictions with reasonable definition of their uncertainty. When multiple models are considered, data collection and analysis focus on evaluation of which model(s) is(are) most supported by the data. Generally, more than one model provides a similar acceptable fit to the observations; thus, inference should be made from multiple models. Kullback-Leibler (K-L) information provides a rigorous foundation for model inference that is simple to compute, is easy to interpret, selects parsimonious models, and provides a more realistic measure of precision than evaluation of any one model or evaluation based on other commonly referenced model selection criteria. These alternative criteria strive to identify the true (or quasi-true) model, assume it is represented by one of the models in the set, and given their preference for parsimony regardless of the available number of observations the selected model may be underfit. This is in sharp contrast to the K-L information approach, where models are considered to be approximations to reality, and it is expected that more details of the system will be revealed when more data are available. We provide a simple, computer-generated example to illustrate the procedure for multimodel inference based on K-L information and present arguments, based on statistical underpinnings that have been overlooked with time, that its theoretical basis renders it preferable to other approaches.
机译：水文地质条件的不确定性使得考虑备选可行模型以评估地下水系统的特征，保持简约性以及对不确定性进行合理定义的预测变得很重要。当考虑多个模型时，数据收集和分析的重点是评估数据最支持哪种模型。通常，一个以上的模型可以为观察提供相似的可接受的拟合度。因此，应该从多个模型进行推断。 Kullback-Leibler（KL）信息为模型推理提供了严格的基础，该模型易于计算，易于解释，选择简约模型，并且比对任何一个模型的评估或基于其他常用参考的评估提供了更为实际的精度度量。型号选择标准。这些替代标准努力确定真实（或准真实）模型，假设它由集合中的一个模型表示，并且考虑到简约性，不管选择的观测可用数目多少，它们都会优先考虑简约性。这与K-L信息方法形成鲜明对比，后者将模型视为逼真的模型，并且可以预期，当有更多数据可用时，系统的更多细节将被揭示。我们提供了一个简单的计算机生成的示例，以说明基于K-L信息的多模型推理过程以及基于随着时间推移而被忽视的统计基础的当前参数，该模型的理论基础使其优于其他方法。

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