Split variational inference

11 years 5 months ago
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.
Guillaume Bouchard, Onno Zoeter
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where ICML
Authors Guillaume Bouchard, Onno Zoeter
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