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Rasmussen, Carl Edward. "Gaussian processes for machine learning." (2006). Raissi, Maziar, and George Karniadakis. "Deep Multi-fidelity Gaussian Processes." arXiv preprint arXiv:1604.07484 (2016). Raissi, Maziar, and George Em Karniadakis. "Machine Learning of Linear Differential Equations using Gaussian Processes." arXiv preprint arXiv:1701 ... Chapter 5 Gaussian Process Regression. Here the goal is humble on theoretical fronts, but fundamental in application. Our aim is to understand the Gaussian process (GP) as a prior over random functions, a posterior over functions given observed data, as a tool for spatial data modeling and surrogate modeling for computer experiments, and simply as a flexible nonparametric regression.