Stein Variational Gaussian Processes. (arXiv:2009.12141v3 [stat.ML] UPDATED)

We show how to use Stein variational gradient descent (SVGD) to carry out
inference in Gaussian process (GP) models with non-Gaussian likelihoods and
large data volumes. Markov chain Monte Carlo (MCMC) is extremely
computationally intensive for these situations, but the parametric assumptions
required for efficient variational inference (VI) result in incorrect inference
when they encounter the multi-modal posterior distributions that are common for
such models. SVGD provides a non-parametric alternative to variational
inference which is substantially faster than MCMC. We …

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