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arXiv · 1506.04000

MCMC for Variationally Sparse Gaussian Processes

James Hensman·Alexander G. de G. Matthews·Maurizio Filippone·Zoubin Ghahramani
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Abstract

Gaussian 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.

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James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani. 2015-06-12. MCMC for Variationally Sparse Gaussian Processes. https://arxiv.org/abs/1506.04000

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