Dividing Local Gaussian Processes for Online Learning Regression
Description
We implement and extend the Dividing Local Gaussian Process
algorithm by Lederer et al. (2020) . Its
main use case is in online learning where it is used to train a network of
local GPs (referred to as tree) by cleverly partitioning the input space.
In contrast to a single GP, 'GPTreeO' is able to deal with larger amounts of
data. The package includes methods to create the tree and set its
parameter, incorporating data points from a data stream as well as making
joint predictions based on all relevant local GPs.