  Bayesian methods estimate a measure of uncertainty by using the posterior
distribution. One source of difficulty in these methods is the computation of
the normalizing constant. Calculating exact posterior is generally intractable
and we usually approximate it. Variational Inference (VI) methods approximate
the posterior with a distribution usually chosen from a simple family using
optimization. The main contribution of this work is described is a set of
update rules for natural gradient variational inference with mixture of
Gaussians, which can be run independently for each of the mixture components,
potentially in parallel.
