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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.
Author Information
Carl Jidling (Uppsala University)
Niklas Wahlström (Uppsala University)
Adrian Wills (University of Newcastle, Australia)
Adrian Wills was born in Orange, N.S.W., Australia and received his B.E. (Elec.) and Ph.D. degrees from The University of Newcastle, Australia (Callaghan Campus) in May 1999 and May 2003, respectively. Since then he has held postdoctoral research positions at Newcastle and spent three years working in industry. The focus of his research has been in the areas of system identification and model predictive control. In July 2015, he returned to the University of Newcastle to lead the Mechatronics Engineering program.
Thomas Schön (Uppsala University)
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