| Eds. T. Morita, M. Suzuki, K. Wada, and M. Kaburagi. Foundations and applications of cluster variation method and path probability method. In Prog. Theor. Phys. Supplement, volume 115, 1994. |
....a region graph in order to ensure that the resulting approximations are accurate. In our original work introducing GBP algorithms [17] we focused on a sub class of GBP algorithms that were equivalent to free energy approximations based on Kikuchi s cluster variation method [16] 18] [19]. We shall show that this method is only one of a variety of methods to generate region graphs and their corresponding free energies and message passing algorithms. In our original work, we also focused on graphical models defined in terms of pair wise or higher order Markov random fields (MRFs) ....
....that path, and will instead describe a quite different approach to approximating the next section; one which underlies the BP algorithm. IV. REGION BASED FREE ENERGY APPROXIMATIONS Kikuchi and the other physicists who further developed the so called cluster variation method [16] 18] [19] introduced a class of approximations to the Gibbs free energy . The idea behind these approximations is similar, but slightly different from the mean field approximation. Whereas the factorized mean field free energy is a function of single node beliefs Z ( in a Kikuchi ....
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Eds. T. Morita, M. Suzuki, K. Wada, and M. Kaburagi. Foundations and applications of cluster variation method and path probability method. In Prog. Theor. Phys. Supplement, volume 115, 1994.
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Eds. T. Morita, M. Suzuki, K. Wada, and M. Kaburagi. Foundations and applications of cluster variation method and path probability method. In Prog. Theor. Phys. Supplement, volume 115, 1994.
No context found.
Eds. T. Morita, M. Suzuki, K. Wada, and M. Kaburagi. Foundations and applications of cluster variation method and path probability method. In Prog. Theor. Phys. Supplement, volume 115, 1994.
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