An Application of Tree-Structured Expectation Propagation for Channel Decoding

Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)

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Pablo Olmos, Luis Salamanca, Juan Fuentes, Fernando Pérez-Cruz


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.