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Gaussian Process Product Models for Nonparametric Nonstationarity

Summary: Gaussian Process Product Models for Nonparametric
Ryan Prescott Adams rpa23@cam.ac.uk
Oliver Stegle os252@cam.ac.uk
Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK
Stationarity is often an unrealistic prior as-
sumption for Gaussian process regression.
One solution is to predefine an explicit non-
stationary covariance function, but such co-
variance functions can be difficult to spec-
ify and require detailed prior knowledge of
the nonstationarity. We propose the Gaus-
sian process product model (GPPM) which
models data as the pointwise product of two
latent Gaussian processes to nonparametri-
cally infer nonstationary variations of ampli-
tude. This approach differs from other non-
parametric approaches to covariance function
inference in that it operates on the outputs


Source: Adams, Ryan Prescott - Department of Electrical and Computer Engineering, University of Toronto


Collections: Computer Technologies and Information Sciences