图 书 馆
基于广义线性模型的图形化模型Graphical Models via Generalized Linear Models |
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| 课程网址: | http://videolectures.net/nips2012_yang_models/ |
| 主讲教师: | Eunho Yang |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2013-01-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Undirected graphical models, or Markov networks, such as Gaussian graphical models and Ising models enjoy popularity in a variety of applications. In many settings, however, data may not follow a Gaussian or binomial distribution assumed by these models. We introduce a new class of graphical models based on generalized linear models (GLM) by assuming that node-wise conditional distributions arise from exponential families. Our models allow one to estimate networks for a wide class of exponential distributions, such as the Poisson, negative binomial, and exponential, by fitting penalized GLMs to select the neighborhood for each node. A major contribution of this paper is the rigorous statistical analysis showing that with high probability, the neighborhood of our graphical models can be recovered exactly. We provide examples of high-throughput genomic networks learned via our GLM graphical models for multinomial and Poisson distributed data. |
| 关 键 词: | 图形模型; 马尔可夫网络; 广义线性模型; 指数分布 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-06：zyk |
| 阅读次数: | 42 |