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An Application of Tree-Structured Expectation Propagation for Channel Decoding
Pablo M Olmos · Luis Salamanca · Juan J. Murillo-Fuentes · Fernando Perez-Cruz

Mon Dec 12 10:00 AM -- 02:59 PM (PST) @

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.

Author Information

Pablo M Olmos (Universidad de Sevilla)
Luis Salamanca (University of Sevilla)
Juan J. Murillo-Fuentes (Universidad de Sevilla)
Fernando Perez-Cruz (Swiss Data Science Center (ETH Zurich and EPFL))

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