Generalized Matheron Variational Implicit Processes
GMVIP is a method for making predictions when you have an implicit prior—a distribution over functions where you don't know the function's density. It works by using a pathwise variational family and an empirical correction term derived from prior samples to construct posterior function samples. This allows it to retain the structure of the original implicit process, performing well in forecasting tasks.