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为列联表分析的贝叶斯组套索

The Bayesian Group-Lasso for Analyzing Contingency Tables
课程网址: http://videolectures.net/icml09_raman_bgl/  
主讲教师: Sudhir Raman
开课单位: 巴塞尔大学
开课时间: 2009-08-26
课程语种: 英语
中文简介:
群拉索估计在许多应用中都很有用,但缺乏对回归系数有意义的方差估计。为了克服这些问题,我们提出了一种对群拉索的全贝叶斯处理方法,利用层次展开法对标准的贝叶斯拉索进行了扩展。然后利用高效的MCMC算法将该方法应用于列联表的泊松模型。仿真实验验证了该方法在地面真实性已知的人工数据集上的性能。当应用于乳腺癌数据集时,该方法证明了识别不同患者组之间标记蛋白相互作用模式差异的能力。
课程简介: Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.
关 键 词: 组套索估计; 回归系数; 方差估计; 贝叶斯套索
课程来源: 视频讲座网
最后编审: 2019-12-07:lxf
阅读次数: 68