Title: Linearly constrained Gaussian processes,
Abstract: We consider a modification of the covariance function in Gaussian processes
to correctly account for known linear constraints. By modelling the target
function as a transformation of an underlying function, the constraints are
explicitly incorporated in the model such that they are guaranteed to be
fulfilled by any sample drawn or prediction made. We also propose a
constructive procedure for designing the transformation operator and illustrate
the result on both simulated and real-data examples.