二分图的贝叶斯非参数模型][Bayesian nonparametric models for bipartite graphs]

二分图的贝叶斯非参数模型 Bayesian nonparametric models for bipartite graphs


课程网址:	http://videolectures.net/nips2012_caron_graphs/
主讲教师:	François Caron
开课单位:	INRIA研究机构
开课时间:	2013-01-16
课程语种:	英语
中文简介:	本文提出了一种新的随机二部图的贝叶斯非参数模型。该模型基于完全随机测度理论，能够处理可能无穷多的节点。我们证明该模型具有吸引人的特性，特别是它可能表现出幂律行为。我们推导了一个后验特性，一个用于网络增长的印度自助餐式生成过程，以及一个用于后验模拟的简单有效的吉布斯采样器。我们的模型很好地适用于几个现实社会网络。
课程简介:	We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on the theory of completely random measures and is able to handle a potentially infinite number of nodes. We show that the model has appealing properties and in particular it may exhibit a power-law behavior. We derive a posterior characterization, an Indian Buffet-like generative process for network growth, and a simple and efficient Gibbs sampler for posterior simulation. Our model is shown to be well fitted to several real-world social networks.
关键词:	贝叶斯非参数模型; 二分图; 社交网络
课程来源:	视频讲座网
最后编审:	2020-07-31：yumf
阅读次数:	87

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