当前位置: X-MOL 学术IEEE Trans. Signal Process. › 论文详情
On the Sample Complexity of Graphical Model Selection From Non-Stationary Samples
IEEE Transactions on Signal Processing ( IF 5.230 ) Pub Date : 2019-11-28 , DOI: 10.1109/tsp.2019.2956687
Nguyen Tran; Oleksii Abramenko; Alexander Jung

We study conditions that allow accurate graphical model selection from non-stationary data. The observed data is modelled as a vector-valued zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This model contains as special cases the standard setting of i.i.d. samples as well as the case of samples forming a stationary time series. More generally, our approach applies to any data for which efficient decorrelation transforms, such as the Fourier transform for stationary time series, are available. By analyzing a conceptually simple model selection method, we derive a sufficient condition on the required sample size for accurate graphical model selection based on non-stationary data.
更新日期:2020-01-04

 

全部期刊列表>>
Springer Nature 2019高下载量文章和章节
化学/材料学中国作者研究精选
《科学报告》最新环境科学研究
ACS材料视界
自然科研论文编辑服务
中南大学国家杰青杨华明
南开大学陈弓课题组招聘启事
中南大学
材料化学和生物传感方向博士后招聘
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug