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2003

Approximate Inference and Constrained Optimization

11 years 11 months ago
Approximate Inference and Constrained Optimization
Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms correspond to extrema of the Bethe and Kikuchi free energy (Yedidia et al., 2001). However, belief propagation does not always converge, which motivates approaches that explicitly minimize the Kikuchi/Bethe free energy, such as CCCP (Yuille, 2002) and UPS (Teh and Welling, 2002). Here we describe a class of algorithms that solves this typically non-convex constrained minimization problem through a sequence of convex constrained minimizations of upper bounds on the Kikuchi free energy. Intuitively one would expect tighter bounds to lead to faster algorithms, which is indeed convincingly demonstrated in our simulations. Several ideas are applied to obtain tight convex bounds that yield dramatic speed-ups over CCCP.
Tom Heskes, Kees Albers, Bert Kappen
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2003
Where UAI
Authors Tom Heskes, Kees Albers, Bert Kappen
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