Compositionally-Warped Gaussian Processes

@article{Tobar2019CompositionallyWarpedGP,
  title={Compositionally-Warped Gaussian Processes},
  author={Felipe A. Tobar and Gonzalo Rios},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2019},
  volume={118},
  pages={
          235-246
        }
}
  • Felipe A. Tobar, Gonzalo Rios
  • Published 23 June 2019
  • Computer Science, Mathematics, Engineering, Medicine
  • Neural networks : the official journal of the International Neural Network Society
The Gaussian process (GP) is a nonparametric prior distribution over functions indexed by time, space, or other high-dimensional index set. The GP is a flexible model yet its limitation is given by its very nature: it can only model Gaussian marginal distributions. To model non-Gaussian data, a GP can be warped by a nonlinear transformation (or warping) as performed by warped GPs (WGPs) and more computationally-demanding alternatives such as Bayesian WGPs and deep GPs. However, the WGP requires… 
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