  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.
