Latent Gaussian Process Regression

Erik Bodin, Carl Henrik Ek

Research output: Contribution to journalArticle (Academic Journal)


We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training data. We show how our approach can be used to model multi-modal and non-stationary processes. We exemplify the approach on a set of synthetic data and provide results on real data from motion capture and geostatistics.
Original languageUndefined/Unknown
Publication statusPublished - 18 Jul 2017


  • stat.ML
  • cs.LG

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