Loading GPFDA package | Loading co2 data | Using exponential covariance kernel | Using a customised covariance kernel | Defining a customised covariance kernel | Defining the first derivatives | Defining the second derivatives | Defining a diagonal matrix including the signal variance | Fitting the GPR model with the customised kernel and making predictions

Plot of raw data | FR fit for training data | GPFR fit for training data | Type I prediction: $y_{M+1}$ observed | Type I prediction: $y_{M+1}$ partially observed | Type II prediction: $y_{M+1}$ not observed

Simulating data from a GP with unidimensional input | Estimation | Prediction

Simulating data from a GP with 2-dimensional input | Estimation | Prediction

Simulating data from a multiple GP with three outputs

Simulating data from a GP with nonstationary and nonseparable covariance structure | Estimating NSGP covariance function | Prediction | Plots of covariance functions