  Machine learning models can be improved by adapting them to respect existing
background knowledge. In this paper we consider multitask Gaussian processes,
with background knowledge in the form of constraints that require a specific
sum of the outputs to be constant. This is achieved by conditioning the prior
distribution on the constraint fulfillment. The approach allows for both linear
and nonlinear constraints. We demonstrate that the constraints are fulfilled
with high precision and that the construction can improve the overall
prediction accuracy as compared to the standard Gaussian process.
