Variational bridge constructs for approximate Gaussian process regression
arXiv:1901.01727
Abstract
This paper introduces a method to approximate Gaussian process regression by representing the problem as a stochastic differential equation and using variational inference to approximate solutions. The approximations are compared with full GP regression and generated paths are demonstrated to be indistinguishable from GP samples. We show that the approach extends easily to non-linear dynamics and discuss extensions to which the approach can be easily applied.
arXiv comment4 pages, presented at BNP@NeurIPS 2018
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