累积配电网及其导数-和-积算法
Cumulative distribution networks and the derivative-sum-product algorithm
摘要：
我们介绍了一种新型的图形模型，称为“累积分布网络”（CDN），它表示一个联合累积分布作为一个产品的本地功能。每个局部函数都可以被视为提供关于变量可能的排序或排名的证据。有趣的是，我们发现CDN的条件独立性与其他图形模型有很大的不同。我们还描述了一个消息传递算法，有效地计算条件累积分布。由于CDN的独特的独立性属性，这些消息通常不与在标准算法（诸如置信传播）中交换的消息一一对应。我们使用先前研究的多玩家游戏数据集演示了CDN在结构化排名学习中的应用。
英文摘要：
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.
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