  This article advocates the use of conformal prediction (CP) methods for
Gaussian process (GP) interpolation to enhance the calibration of prediction
intervals. We begin by illustrating that using a GP model with parameters
selected by maximum likelihood often results in predictions that are not
optimally calibrated. CP methods can adjust the prediction intervals, leading
to better uncertainty quantification while maintaining the accuracy of the
underlying GP model. We compare different CP variants and introduce a novel
variant based on an asymmetric score. Our numerical experiments demonstrate the
effectiveness of CP methods in improving calibration without compromising
accuracy. This work aims to facilitate the adoption of CP methods in the GP
community.
