Linearly constrained Gaussian processes

Part of Advances in Neural Information Processing Systems 30 (NIPS 2017)

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Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön


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.