First, inference under arbitrary priors can become complex.
in the simple case of our diagonal covariance Gaussians, the gradient of the observed data likelihood can be computed directly using the DMV's expected counts and maximum-likelihood estimation can be accomplished by applying standard gradient optimization methods.