The Gaussian process (GP) is a nonparametric prior distribution over functions indexed by
time, space, or other high-dimensional index set. The GP is a flexible model yet its limitation …

Gaussian processes (GPs) can be used to predict future states of a system with credible
intervals when considering multiple previous trajectories for training. For example, predicting …

Mass balance assessments of the East Antarctic ice sheet (EAIS) are highly sensitive to
changes in firn thickness, causing substantial disagreement in estimates of its contribution to …

Solar cycle prediction is a key activity in space weather research. Several techniques have
been employed in recent decades in order to try to forecast the next sunspot-cycle maxima …

Large-scale statistical modeling has become necessary with the vast flood of geospace data
coming from various sources. In space statistics, the Maximum Likelihood Estimation (MLE) …

Maximum likelihood estimation is an essential tool in the procedure to impute missing data
in climate/weather applications. By defining a particular statistical model, the maximum …

This paper proposes the use of Gaussian Process Regression to automatically identify
relevant predictor variables in a formulation of a remaining useful life model for unmonitored …

During the development of aerospace structures, typically many fatigue tests are conducted.
During these tests, much effort is put into inspections in order to detect the onset of failure …

In a number of data-driven applications such as detection of arrhythmia, interferometry or
audio compression, observations are acquired indistinctly in the time or frequency domains …

Data transformations are essential for broad applicability of parametric regression models.
However, for Bayesian analysis, joint inference of the transformation and model parameters …