Automated Variational Inference for Gaussian Process Models

no code implementations • NeurIPS 2014 • Trung V. Nguyen, Edwin V. Bonilla

Using a mixture of Gaussians as the variational distribution, we show that (i) the variational objective and its gradients can be approximated efficiently via sampling from univariate Gaussian distributions and (ii) the gradients of the GP hyperparameters can be obtained analytically regardless of the model likelihood.

Variational Inference