Graphical Modeling for High Dimensional Data

被引：0

作者：

Begum, Munni ^{[1
]}

Bagga, Jay ^{[2
]}

Blakey, C. Ann ^{[3
]}

机构：

[1] Ball State Univ, Dept Math Sci, Stat, Muncie, IN 47306 USA

[2] Ball State Univ, Comp Sci, Muncie, IN 47306 USA

[3] Ball State Univ, Dept Biol, Genet, Muncie, IN 47306 USA

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2012年 / 11卷 / 02期

关键词：

High dimensional data; graphical Markov models; conditional independence; Markov properties; chain graphs;

D O I：

10.22237/jmasm/1351743360

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

With advances in science and information technologies, many scientific fields are able to meet the challenges of managing and analyzing high-dimensional data. A so-called large p small n problem arises when the number of experimental units, n, is equal to or smaller than the number of features, p. A methodology based on probability and graph theory, termed graphical models, is applied to study the structure and inference of such high-dimensional data.

引用

页码：457 / 468

页数：12

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