The parameters of the models are given in the Table below. Note that the trend parameters in \(\boldsymbol{\beta}\) are of a somewhat different nature than the other ones. The parameters \(\beta_k\) can best be compared to the values \(\zeta(\mathbf{x}_i)\) of the unobserved GP. Indeed if no nugget or noise is used, the estimation of \(\boldsymbol{\beta}\) is the same thing as the estimation of \(\boldsymbol{\zeta}\).

The trend parameters \(\beta_j\) never appear in the objective function used to fit the models, be it of frequentist or Bayesian nature.


GP Cov





\([\boldsymbol{\theta}, \, \sigma^2]\)




\([\boldsymbol{\theta}, \, \sigma^2]\)


\([\boldsymbol{\theta}, \,\alpha]\), \(\alpha:=\sigma^2/(\sigma^2 + \tau^2)\)



\([\boldsymbol{\theta}, \, \sigma^2]\)


\([\boldsymbol{\theta}, \, \sigma^2]\)

Parameters used for the trend, the smooth GP and the noise or nugget parts. The column Optim is for the parameters used in the optimization. The other parameters are either known as \([\tau_i^2]\) or marginalized out, or replaced by their MLE \(\boldsymbol{\beta}\).}

Functional point of view

For the models used with libKriging, both the trend functions and the covariance kernel have an impact. While a GP model for \(\zeta(\mathbf{x})\) relates to a covariance kernel and to the corresponding Reproducing Kernel Hilbert Space (RKHS), a Kriging model as described in Kriging models relates to a semi-RKHS Berlinet and Thomas-Agnan [BTA04]. This space \(\mathcal{H}\) is a semi-Hilbert space of functions in which the trend functions \(f_k\) generate a finite-dimensional linear subspace \(\mathcal{F}\) called the nullspace which contains so-called unpenalized functions i.e., functions with (semi) norm zero.

When the covariance parameters are known, Kriging provides as the Kriging mean the function \(h \in \mathcal{H}\) which minimises the Penalized Sum of Squares (PSS) criterion

\[ \label{eq:PSS} \texttt{PSS} := \frac{1}{\tau^2} \, \sum_{i=1}^n \{y_i - h(\mathbf{x}_i)\}^2 + \| h \|_{\mathcal{H}}^2. \]

In the case where no nugget is used (corresponding to \(\tau^2 \to 0\)), the discrete sum in the \(\texttt{PSS}\) criterion is actually zero at the optimum so that \(h\) interpolates the data and has minimal norm \(\|.\|_{\mathcal{H}}\) amongst the functions \(h \in \mathcal{H}\) that interpolates the data. We may regard Kriging as using a prior on a functional space, with an implied non-informative prior for the trend part. At the right-hand side of the equation above, the first term can be regarded as \(-2 \log L\) where \(L\) is the likelihood while the square norm can formally be regarded as \(-2 \log \pi(h)\) where \(\pi(h)\) is a prior density, although this is not tenable from a theoretical point of view. By minimizing \(\texttt{PSS}\), we get the function \(h\) with maximum posterior density which is also the posterior mean.

The Kriging framework is similar to the splines framework, but as opposed to the later one, the trend functions are chosen quite arbitrarily and may also belong to the RKHS of the kernel. This will indeed be the case when \(d=1\) and a constant trend is used with one of the kernels available in libKriging: the constant trend function is therefore unpenalized, which makes the Kriging smoothing and the Kriging prediction behaves well w.r.t. a translation of the observations \(\mathbf{y} \to \mathbf{y} + \text{Cst}\): the predicted values are then translated similarly. The function \(h \in \mathcal{H}\) minimizing the criterion \(\texttt{PSS}\) above can be written in a non-unique way as

\[ h(\mathbf{x}) = \sum_{i=1}^n \alpha_i \, C(\mathbf{x}_i, \, \mathbf{x}) + \sum_{k=1}^p \beta_k f_k(\mathbf{x}), \]

and Kriging indeeds find suitable vectors \(\boldsymbol{\alpha}\) and \(\boldsymbol{\beta}\). The representation of \(h\) can be made unique by imposing orthogonality constraints, see The Bending Energy Matrix. See Wahba [Wah78] for the use of an improper prior on the coefficients of the trend functions.

Note Allowing for a non-informative trend has an important implication in terms of implementation since Universal Kriging equations must be used. By contrast, an informative trend can be coped with by using only Simple Kriging equations and sum of kernels. Indeed the informative trend corresponds to a kernel of the form \(\mathbf{f}(\mathbf{x})^\top \mathbf{A}\mathbf{f}(\mathbf{x})\) where \(\mathbf{A}\) is a \(p \times p\) positive definite matrix. The informative approach is most often retained in the Machine Learning community.