 Gaussian process approximations

Gaussian process (GP) approximation is a powerful statistical technique utilized primarily in the context of machine learning and Bayesian statistics for function approximation, regression, and optimization. A Gaussian process is a collection of random variables, any finite number of which have a joint Gaussian distribution. It is particularly appealing due to its flexibility in modeling complex functions and the uncertainty associated with them.