Gaussian Kullback-Leibler Approximate Inference
Edward Challis, David Barber; 14(32):2239−2286, 2013.
We investigate Gaussian Kullback-Leibler (G-KL) variational approximate inference techniques for Bayesian generalised linear models and various extensions. In particular we make the following novel contributions: sufficient conditions for which the G-KL objective is differentiable and convex are described; constrained parameterisations of Gaussian covariance that make G-KL methods fast and scalable are provided; the lower bound to the normalisation constant provided by G-KL methods is proven to dominate those provided by local lower bounding methods; complexity and model applicability issues of G-KL versus other Gaussian approximate inference methods are discussed. Numerical results comparing G-KL and other deterministic Gaussian approximate inference methods are presented for: robust Gaussian process regression models with either Student-$t$ or Laplace likelihoods, large scale Bayesian binary logistic regression models, and Bayesian sparse linear models for sequential experimental design.
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