Summary: A Gaussian process (GP) can be thought of as an infinite collection of random variables with the property that any subset, say of dimension \(n\), of these variables have a multivariate normal distribution of dimension \(n\), mean vector \(\beta \) and covariance matrix \(\Sigma \) [A. O’Hagan and J. Forster, Kendall’s advanced theory of statistics. Vol. 2 B, Bayesian inference. 2nd ed., Wiley (2004; Zbl 1058.62002)]. The elements of the covariance matrix are routinely specified through the multiplication of a common variance by a correlation function. It is important to use a correlation function that provides a valid covariance matrix (positive definite). Further, it is well known that the smoothness of a GP is directly related to the specification of its correlation function. Also, from a Bayesian point of view, a prior distribution must be assigned to the unknowns of the model. Therefore, when using a GP to model a phenomenon, the researcher faces two challenges: the need of specifying a correlation function and a prior distribution for its parameters.
In the literature there are many classes of correlation functions which provide a valid covariance structure. Also, there are many suggestions of prior distributions to be used for the parameters involved in these functions. We aim to investigate how sensitive the GPs are to the (sometimes arbitrary) choices of their correlation functions. For this, we have simulated 25 sets of data each of size 64 over the square \([0, 5]\times[0, 5]\) with a specific correlation function and fixed values of the GP’s parameters. We then fit different correlation structures to these data, with different prior specifications and check the performance of the adjusted models using different model comparison criteria.