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.
Begum, Munni; Bagga, Jay; and Blakey, C. Ann
"Graphical Modeling for High Dimensional Data,"
Journal of Modern Applied Statistical Methods:
2, Article 17.
Available at: http://digitalcommons.wayne.edu/jmasm/vol11/iss2/17