<ul><li>Researchers propose an analytical approximation for estimating the gradient of the Evidence Lower Bound (ELBO) in variational inference problems.</li><li>The method specifically addresses the clutter problem, where a Bayesian network consists of observations drawn from a mixture of a Gaussian distribution embedded in unrelated clutter.</li><li>The proposed solution utilizes the reparameterization trick and leverages the assumption that the variational distribution is generally more compactly supported than the Gaussian distribution in the likelihood factors, allowing for efficient local approximation.</li><li>The method demonstrates good accuracy, rate of convergence, and linear computational complexity, making it a promising approach in Bayesian inference.</li></ul>

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

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