Linearly constrained Gaussian processes

Title: Linearly constrained Gaussian processes
Creator: Jidling, Carl; Wahlström, Niklas; Wills, Adrian; Schön, Thomas B.
Relation: 31st Conference on Neural Information Processing Systems (NIPS 2017). Advances in Neural Information Processing Systems 30 (NIPS 2017) (Long Beach, CA 4-9 December, 2017)
Relation: https://papers.nips.cc/paper/6721-linearly-constrained-gaussian-processes
Publisher: Neural Information Processing Systems Foundation
Resource Type: conference paper
Date: 2017
Description: We consider a modification of the covariance function in Gaussian processes to correctly account for known linear operator constraints. By modeling 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.
Subject: Gaussian processes; linear operator; covariance functions; Bayesian non-parametric modeling
Identifier: http://hdl.handle.net/1959.13/1391173
Identifier: uon:33170
Language: eng
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