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.

UR - http://www.scopus.com/inward/record.url?scp=56449091550&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=56449091550&partnerID=8YFLogxK

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 -