  We propose an analytical solution 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. The method employs the reparameterization trick to move the
gradient operator inside the expectation and relies on the assumption that,
because the likelihood factorizes over the observed data, the variational
distribution is generally more compactly supported than the Gaussian
distribution in the likelihood factors. This allows efficient local
approximation of the individual likelihood factors, which leads to an
analytical solution for the integral defining the gradient expectation. We
integrate the proposed gradient approximation as the expectation step in an EM
(Expectation Maximization) algorithm for maximizing ELBO and test against
classical deterministic approaches in Bayesian inference, such as the Laplace
approximation, Expectation Propagation and Mean-Field Variational Inference.
The proposed method demonstrates good accuracy and rate of convergence together
with linear computational complexity.
