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• W2951088868 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." @default.
• W2951088868 created "2019-06-27" @default.
• W2951088868 creator A5010820865 @default.
• W2951088868 creator A5021162375 @default.
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• W2951088868 date "2015-06-12" @default.
• W2951088868 modified "2023-09-27" @default.
• W2951088868 title "MCMC for Variationally Sparse Gaussian Processes" @default.
• W2951088868 cites W137285897 @default.
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• W2951088868 hasPublicationYear "2015" @default.
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