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Exact inference and learning for cumulative distribution functions on loopy graphs
We compare the performance of the resulting algorithm to Mathematica and D*, and we apply our method to learning models for rainfall and H1N1 data, where we show that CDNs with cycles are able to provide a significantly better fits to the data as compared to tree-structured and unstructured CDNs and other heavy-tailed multivariate distributions such as the multivariate copula and logistic models.
Structured ranking learning using cumulative distribution networks
Ranking is at the heart of many information retrieval applications.
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