Implementation for the first Figure of the article "Gaussian process interpolation with conformal prediction: methods and comparative analysis"

First, create a virtual environment then install the requirements. Finally, install GPMP package to build Gaussian process models.

The file notebook goldstein_price_cloud shows how to create the points cloud in the article for the Godlstein-Price function.

You can create other cloud for functions using the test functions implemented in the module gpmp.misc.testfunctions or using the functions in src.functions.py.

The cloud is built using the class GPExperiment implemented in the file src.gpmodelmetrics.py. When you instantiate the class, a design of experiment is automatically created. Then you can use the two above methods:

• The evaluate_model_variation method is used to generate the cloud around the parameter selected by restricted maximum likelihood.
• The j_plus_gp_point method is used to compute the IAE when the prediction interval are computed using J+GP method.