Document Type
Article
Publication Date
2019
First Page
1
Last Page
9
Abstract
This report tends to provide details on how to perform predictions using Gaussian process regression (GPR) modeling. In this case, we represent proofs for prediction using non-parametric GPR modeling for noise-free predictions as well as prediction using semi-parametric GPR for noisy observations.
Recommended Citation
Shekaramiz, Mohammad; Moon, Todd K.; and Gunther, Jacob H., "Details on Gaussian Process Regression (GPR) and Semi-GPR Modeling" (2019). Electrical and Computer Engineering Faculty Publications. Paper 216.
https://digitalcommons.usu.edu/ece_facpub/216
