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Gaussian processes (GPs) are a popular model for spatially referenced data and allow descriptive statements, predictions at new locations, and simulation of new fields. Often a few parameters are sufficient to parameterize the covariance function, and maximum likelihood (ML) methods can be used to estimate these parameters from data. ML methods, ho...
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... of ecient approximate statistical approaches may explain why some applied scientists turn away from GP models in favor of algorithmic methods designed for speciic tasks. For example, there are many recent algorithms for the fast prediction of missing observations in satellite data; see Table 1 in Gerber, de Jong, Schaepman, Schaepman-Strub, and Furrer (2018) for an overview. Such methods are typically much faster but provide only a limited statistical framework, which makes uncertainty quantiication diicult. ...Similar publications
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