Split variational inference

We propose a deterministic method to evaluate the integral of a positive function based on soft-binning functions that smoothly cut the integral into smaller integrals that are easier to approximate. In combination with mean-field approximations for each individual sub-part this leads to a tractable algorithm that alternates between the optimization of the bins and the approximation of the local integrals. We introduce suitable choices for the binning functions such that a standard mean field approximation can be extended to a split mean field approximation without the need for extra derivations. The method can be seen as a revival of the ideas underlying the mixture mean field approach. The latter can be obtained as a special case by taking soft-max functions for the binning.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Guillaume Bouchard, Onno Zoeter
• Keywords: Approximation, ICML 2009, Machine Learning, Mean field Approach, Mean field Approximation