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Tutorial: The Art of Gaussian Processes: Classical and Contemporary

Beyond Gaussian Likelihood

César Lincoln Mattos


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.