Editing Gaussian process (section)

== Computational issues ==
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{{See also|Gaussian process approximations}}
In practical applications, Gaussian process models are often evaluated on a grid leading to multivariate normal distributions. Using these models for prediction or parameter estimation using maximum likelihood requires evaluating a multivariate Gaussian density, which involves calculating the determinant and the inverse of the covariance matrix. Both of these operations have cubic computational complexity which means that even for grids of modest sizes, both operations can have a prohibitive computational cost. This drawback led to the development of multiple [[Gaussian process approximations|approximation methods]].<ref name = "highDimBayesianGeostat"></ref>