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Overview of Explicit Multiscale Models

Part of the Springer Series in Statistics book series (SSS)

Multiscale modeling arises in a wide variety of applications. As discussed in Chapter 1, there are at least three classes of problems that can be modeled most effectively within a multiscale framework. In the first type, data are observed at different spatial scales and the model is used to integrate the information from the different scales. In the second type, data are observed only at the finest scale and the model is used to induce a particular process at that scale. In the third type, the observed data are related nonlocally and nonlinearly to an underlying multiscale process, and the model is used as a prior for that process.

Keywords

Regression Tree Multiscale Model Resolution Level Coarse Level Multivariate Normal Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

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