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==Gaussian process regression or Kriging==
{{Main|Gaussian process regression}}
In Gaussian process regression, also known as Kriging, a Gaussian prior is assumed for the regression curve. The errors are assumed to have a [[multivariate normal distribution]] and the regression curve is estimated by its [[posterior mode]]. The Gaussian prior may depend on unknown hyperparameters, which are usually estimated via [[empirical Bayes]].
The hyperparameters typically specify a prior covariance kernel. In case the kernel should also be inferred nonparametrically from the data, the [[Information_field_theory#Critical_filter|critical filter]] can be used.
[[Smoothing splines]] have an interpretation as the posterior mode of a Gaussian process regression.
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