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Summary: An Auxiliary Variational Method
Felix V. Agakov1
and David Barber2
1
University of Edinburgh, 5 Forrest Hill, EH1 2QL Edinburgh, UK
felixa@inf.ed.ac.uk, http://anc.ed.ac.uk
2
IDIAP, Rue du Simplon 4, CH-1920 Martigny Switzerland,
david.barber@idiap.ch
Abstract. An attractive feature of variational methods used in the con-
text of approximate inference in undirected graphical models is a rigorous
lower bound on the normalization constants. Here we explore the idea of
using augmented variable spaces to improve on the standard mean-field
bounds. Our approach forms a more powerful class of approximations
than any structured mean field technique. Moreover, the existing varia-
tional mixture models may be seen as computationally expensive special
cases of our method. A byproduct of our work is an efficient way to cal-
culate a set of mixture coefficients for any set of tractable distributions
that principally improves on a flat combination.
1 Introduction
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