图 书 馆
基于广义线性模型的图形模型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: |  非定向图形模型或Markov网络（例如高斯图形模型和Ising模型）在各种应用中都很受欢迎。但是，在许多情况下，数据可能不遵循这些模型假设的高斯或二项式分布。通过假定节点级条件分布来自指数族，我们引入了一种基于广义线性模型（GLM）的新型图形模型。我们的模型允许人们通过拟合惩罚GLM为每个节点选择邻域来估计广泛的指数分布网络，例如泊松，负二项式和指数网络。本文的主要贡献在于严格的统计分析，表明极有可能准确地恢复图形模型的邻域。我们提供了通过GLM图形模型学习的用于多项式和Poisson分布式数据的高吞吐量基因组网络的示例。 |
| 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; 分布式数据 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-25：zyk |
| 最后编审: | 2020-11-25：zyk |
| 阅读次数: | 54 |