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29 March 2024
 
  » arxiv » 1506.4000

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MCMC for Variationally Sparse Gaussian Processes
James Hensman ; Alexander G. de G. Matthews ; Maurizio Filippone ; Zoubin Ghahramani ;
Date 12 Jun 2015
AbstractGaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compute efficiently when the number of data is large; how to approximate the posterior when the likelihood is not Gaussian and how to estimate covariance function parameter posteriors. This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form. The result is a Hybrid Monte-Carlo sampling scheme which allows for a non-Gaussian approximation over the function values and covariance parameters simultaneously, with efficient computations based on inducing-point sparse GPs. Code to replicate each experiment in this paper will be available shortly.
Source arXiv, 1506.4000
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