TY - GEN
T1 - Gaussian process product models for nonparametric nonstationarity
AU - Adams, Ryan Prescott
AU - Stegle, Oliver
PY - 2008
Y1 - 2008
N2 - Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit non-stationary covariance function, but such covariance functions can be difficult to specify and require detailed prior knowledge of the nonstationarity. We propose the Gaussian process product model (GPPM) which models data as the pointwise product of two latent Gaussian processes to nonparametrically infer nonstationary variations of amplitude. This approach differs from other non-parametric approaches to covariance function inference in that it operates on the outputs rather than the inputs, resulting in a significant reduction in computational cost and required data for inference. We present an approximate inference scheme using Expectation Propagation. This variational approximation yields convenient GP hyperparameter selection and compact approximate predictive distributions.
AB - Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit non-stationary covariance function, but such covariance functions can be difficult to specify and require detailed prior knowledge of the nonstationarity. We propose the Gaussian process product model (GPPM) which models data as the pointwise product of two latent Gaussian processes to nonparametrically infer nonstationary variations of amplitude. This approach differs from other non-parametric approaches to covariance function inference in that it operates on the outputs rather than the inputs, resulting in a significant reduction in computational cost and required data for inference. We present an approximate inference scheme using Expectation Propagation. This variational approximation yields convenient GP hyperparameter selection and compact approximate predictive distributions.
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U2 - 10.1145/1390156.1390157
DO - 10.1145/1390156.1390157
M3 - Conference contribution
AN - SCOPUS:56449091550
SN - 9781605582054
T3 - Proceedings of the 25th International Conference on Machine Learning
SP - 1
EP - 8
BT - Proceedings of the 25th International Conference on Machine Learning
PB - Association for Computing Machinery (ACM)
T2 - 25th International Conference on Machine Learning
Y2 - 5 July 2008 through 9 July 2008
ER -