Decoupled Variational Gaussian Inference

Variational Gaussian (VG) inference methods that optimize a lower bound to the marginal likelihood are a popular approach for Bayesian inference. These methods are fast and easy to use, while being reasonably accurate. A difficulty remains in computation of the lower bound when the latent dimensionality L is large. Even though the lower bound is concave for many models, its computation requires optimization over O(L 2) variational parameters. Efficient reparameterization schemes can reduce the number of parameters, but give inaccurate solutions or destroy concavity leading to slow convergence.