Analytical Approximation of the ELBO Gradient for Efficient Variational Inference in the Clutter Problem
An analytical solution is proposed for approximating the gradient of the Evidence Lower Bound (ELBO) in variational inference problems where the statistical model is a Bayesian network consisting of observations drawn from a mixture of a Gaussian distribution embedded in unrelated clutter, known as the clutter problem.