Uncertainty in Artificial Intelligence
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Cumulative distribution networks and the derivative-sum-product algorithm
Jim Huang, Brendan Frey
We introduce a new type of graphical model called a "cumulative distribution network" (CDN), which expresses a joint cumulative distribution as a product of local functions. Each local function can be viewed as providing evidence about possible orderings, or rankings, of variables. Interestingly, we find that the conditional independence properties of CDNs are quite different from other graphical models. We also describe a messagepassing algorithm that efficiently computes conditional cumulative distributions. Due to the unique independence properties of the CDN, these messages do not in general have a one-to-one correspondence with messages exchanged in standard algorithms, such as belief propagation. We demonstrate the application of CDNs for structured ranking learning using a previously-studied multi-player gaming dataset.
Pages: 290-297
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PDF Link: /papers/08/p290-huang.pdf
AUTHOR = "Jim Huang and Brendan Frey",
TITLE = "Cumulative distribution networks and the derivative-sum-product algorithm",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "290--297"

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