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Models for Spatial Data

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

Before launching into the main topics of the book, we first want to introduce two standard models used for spatial data, as they will reappear throughout the book. The first is the Markov random field (MRF), which is most useful for grids and irregular areal data. The second is the Gaussian process, which is more useful when a continuous surface is desired or a wider variety of spatial smoothness needs to be specified or fit.

Keywords

Spatial Data Gaussian Process Markov Random Field Markov Chain Monte Carlo Method Random Realization 
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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