Multilevel Gaussian Processes with Applications to Inverse Problems
We present a multilevel Gaussian process (GP) regression framework for approximating functions that are accessible only through a hierarchy of approximations of increasing accuracy and computational cost. One example is the response surface of a parametrized partial differential equation, where the hierarchy arises fro...