Mixtures of Gaussian Processes for regression under multiple prior distributions
Abstract
When constructing a Bayesian Machine Learning model, we might be faced with multiple different prior distributions and thus are required to properly consider them in a sensible manner in our model. While this situation is reasonably well explored for classical Bayesian Statistics, it appears useful to develop a corresponding method for complex Machine Learning problems. Given their underlying Bayesian framework and their widespread popularity, Gaussian Processes are a good candidate to tackle this task. We therefore extend the idea of Mixture models for Gaussian Process regression in order to work with multiple prior beliefs at once - both a analytical regression formula and a Sparse Variational approach are considered. In addition, we consider the usage of our approach to additionally account for the problem of prior misspecification in functional regression problems.
Cite
@article{arxiv.2104.09185,
title = {Mixtures of Gaussian Processes for regression under multiple prior distributions},
author = {Sarem Seitz},
journal= {arXiv preprint arXiv:2104.09185},
year = {2021}
}