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• W2097615037 abstract "We show an application of a tree structure for approximate inference in graphical models using the expectation propagation algorithm. These approximations are typically used over graphs with short-range cycles. We demonstrate that these approximations also help in sparse graphs with long-range loops, as the ones used in coding theory to approach channel capacity. For asymptotically large sparse graph, the expectation propagation algorithm together with the tree structure yields a completely disconnected approximation to the graphical model but, for for finite-length practical sparse graphs, the tree structure approximation to the code graph provides accurate estimates for the marginal of each variable. Furthermore, we propose a new method for constructing the tree structure on the fly that might be more amenable for sparse graphs with general factors." @default.
• W2097615037 created "2016-06-24" @default.
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• W2097615037 date "2011-12-12" @default.
• W2097615037 modified "2023-10-01" @default.
• W2097615037 title "An Application of Tree-Structured Expectation Propagation for Channel Decoding" @default.
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