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Linearly Constrained Gaussian Processes with Boundary Conditions
[article]
2021
arXiv
pre-print
One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear partial differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations in the solution set of such systems, in particular only such solutions can be represented by Gaussian process regression. The construction is fully algorithmic via Gr\"obner bases and it
arXiv:2002.00818v3
fatcat:6nmv5k4nyvhxzb7p2bztvmpgbi