Importance Weighting And Variational Inference

Authors:
Justin Domke University of Massachusetts, Amherst
Dan Sheldon University of Massachusetts Amherst

Introduction:

Recent work used importance sampling ideas for better variational bounds on likelihoods.

Abstract:

Recent work used importance sampling ideas for better variational bounds on likelihoods. We clarify the applicability of these ideas to pure probabilistic inference, by showing the resulting Importance Weighted Variational Inference (IWVI) technique is an instance of augmented variational inference, thus identifying the looseness in previous work. Experiments confirm IWVI's practicality for probabilistic inference. As a second contribution, we investigate inference with elliptical distributions, which improves accuracy in low dimensions, and convergence in high dimensions.

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