Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gaussian Graphical Model exploration and selection in high dimension low
  sample size setting   Gaussian Graphical Models (GGM) are often used to describe the conditional
correlations between the components of a random vector. In this article, we
compare two families of GGM inference methods: nodewise edge selection and
penalised likelihood maximisation. We demonstrate on synthetic data that, when
the sample size is small, the two methods produce graphs with either too few or
too many edges when compared to the real one. As a result, we propose a
composite procedure that explores a family of graphs with an nodewise numerical
scheme and selects a candidate among them with an overall likelihood criterion.
We demonstrate that, when the number of observations is small, this selection
method yields graphs closer to the truth and corresponding to distributions
with better KL divergence with regards to the real distribution than the other
two. Finally, we show the interest of our algorithm on two concrete cases:
first on brain imaging data, then on biological nephrology data. In both cases
our results are more in line with current knowledge in each field.

2
The categories of this paper are: math
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci