Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Proceedings of the 1998 conference on Advances in neural information processing systems II
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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Computing marginal probabilities from a high-dimensional distribution is generally an intractable task which arises in various probabilistic information processing. The minimum of Kikuchi free energy is known to yield more accurate marginals than that of Bethe free energy. Concave convex procedure (CCCP) proposed by Yuille is known to be an algorithm which guarantees to monotonically decrease both free energies.In this paper, we generalize CCCP algorithm for Kikuchi free energy and present a new CCCP (NCCCP) algorithm which includes a high-dimensional free vector. The free vector enables NCCCP to be more stable and to reduce enormous computational cost underlying CCCP. Moreover it seems that there exists an optimal series of free vectors that can minimize the huge computational cost. This means that there exists an optimal concave and convex decomposition schedule in Kikuchi free energy.