Combining information across spatial scales
Combining information across spatial scales
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DOI：
10.1198/004017004000000572
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发表时间：
2005-02-01
期刊：
影响因子：
2.5
通讯作者：
Berliner, LM
中科院分区：
文献类型：
--
作者：
Wikle, CK;Berliner, LM
物理学中的空间和时空过程。环境问题。而生物科学往往在不同的时空尺度上呈现出复杂多样的模式。科学认识和观测数据在不同尺度上的形式和内容都各不相同。我们开发和研究的贝叶斯层次结构框架，这些信息源的组合可以完成。我们的方法是针对各种特殊的空间尺度出现的设置。这些尺度可以由数据收集方法、先前信息的可用性和/或分析的目标决定。该方法仅限于几个基本尺度。因此，我们避免了构建一个可以在所有尺度上使用的模型的挑战性问题。这意味着我们只能在预先选定的特殊尺度上提供推论。然而，涉及特殊尺度的问题是足够常见的，以证明我们相对简单的建模和分析策略与形成在所有尺度上有效的模型的艰巨任务之间的权衡。具体来说我们的方法是基于一个简单的想法，即在感兴趣的某个分辨率下，将空间连续过程调节为该过程的面积平均。此外，规定分辨率的数据然后以该面积平均真实过程为条件。这些条件作用的论点很好地符合分层贝叶斯框架。该方法被证明是空间预测的一个重要的量称为流函数的基础上，从卫星观测和气象中心，计算机模型输出的风信息。
Spatial and spatiotemporal processes in the physical. environmental. and biological sciences often exhibit complicated and diverse patterns across different space-time scales. Both scientific understanding and observational data vary in form and content across scales. We develop and examine a Bayesian hierarchical framework by which the combination of such information sources can be accomplished. Our approach is targeted to settings in which various special spatial scales arise. These scales may be dictated by the data collection methods, availability of prior information, and/or goals of the analysis. The approach restricts to a few essential scales. Hence we avoid the challenging problem of constructing a model that can be used at all scales. This means that we can provide inferences only at the preselected special scales. However, problems involving special scales are sufficiently common to justify the trade-off between our comparatively simple modeling and analysis strategy with the formidable task of forming models valid at all scales. Specifically. our approach is based on a simple idea of conditioning the spatially continuous process on an areal average of the process at some resolution of interest. In addition, the data at prescribed resolutions are then conditioned on this areal-averaged true process. These conditioning arguments fit nicely into the hierarchical Bayesian framework. The methodology is demonstrated for the spatial prediction of an important quantity known as streamfunction based on wind information from satellite observations and weather center, computer model output.