Talk
in
Tutorial: The Art of Gaussian Processes: Classical and Contemporary
Beyond Gaussian Likelihood
César Lincoln Mattos
Abstract:
We expand the applicability of GP models by introducing non-Gaussian likelihoods. We show why inference becomes intractable and how it can be approximated. Via the use of illustrations, we focus on the binary classification learning scenario, warping functions, and heteroscedastic models.