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Gaussian Process Product Models

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If you have a question about this talk, please contact Ryan Prescott Adams.

The Gaussian process is an appealing tool for nonlinear Bayesian regression. Many times, however, GPs are used with simple stationary covariance functions that may not reflect true prior beliefs regarding the function being regressed. Specifying nonstationary covariance functions directly, however, can be unintuitive and makes just as strong prior assumptions as the stationary case. In this talk I will present an approach for efficiently modelling nonstationarity in a nonparametric manner via a product of stationary latent Gaussian processes.

This talk is part of the Inference Group series.

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