Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem

arXiv:2404.10550v1 Announce Type: new
Abstract: 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, …

abstract approximation arxiv bayesian context cs.lg distribution embedded evidence gradient inference network solution statistical stat.ml type