Faster algorithms for max-product message-passing
Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, such as the junction-tree algorithm or loopy belief-propagation. The exact solution to this problem is well-known to be exponential in the size of the maximal cliques of the triangulated model, while approximate inference is typically exponential in the size of the model's factors. In this paper, we take advantage of the fact that many models have maximal cliques that are larger than their...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|01_McAuley_Faster_algorithms_for_2011.pdf||1.09 MB||Adobe PDF|
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