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Pseudo-marginal Bayesian inference for Gaussian process latent variable models
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Gadd, Charles W. L., Wade, Sara and Shah, A. A. (2021) Pseudo-marginal Bayesian inference for Gaussian process latent variable models. Machine Learning : Science and Technology, 110 (6). pp. 1105-1143. doi:10.1007/s10994-021-05971-2 ISSN 2632-2153.
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Official URL: http://dx.doi.org/10.1007/s10994-021-05971-2
Abstract
A Bayesian inference framework for supervised Gaussian process latent variable models is introduced. The framework overcomes the high correlations between latent variables and hyperparameters by collapsing the statistical model through approximate integration of the latent variables. Using an unbiased pseudo estimate for the marginal likelihood, the exact hyperparameter posterior can then be explored using collapsed Gibbs sampling and, conditional on these samples, the exact latent posterior can be explored through elliptical slice sampling. The framework is tested on both simulated and real examples. When compared with the standard approach based on variational inference, this approach leads to significant improvements in the predictive accuracy and quantification of uncertainty, as well as a deeper insight into the challenges of performing inference in this class of models.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Journal or Publication Title: | Machine Learning : Science and Technology | ||||||||
Publisher: | IOP Publishing Ltd | ||||||||
ISSN: | 2632-2153 | ||||||||
Official Date: | June 2021 | ||||||||
Dates: |
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Volume: | 110 | ||||||||
Number: | 6 | ||||||||
Page Range: | pp. 1105-1143 | ||||||||
DOI: | 10.1007/s10994-021-05971-2 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
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