English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Gaussian Graphical Models (GGM) are often used to describe the conditionalcorrelations between the components of a random vector. In this article, wecompare two families of GGM inference methods: nodewise edge selection andpenalised likelihood maximisation. We demonstrate on synthetic data that, whenthe sample size is small, the two methods produce graphs with either too few ortoo many edges when compared to the real one. As a result, we propose acomposite procedure that explores a family of graphs with an nodewise numericalscheme and selects a candidate among them with an overall likelihood criterion.We demonstrate that, when the number of observations is small, this selectionmethod yields graphs closer to the truth and corresponding to distributionswith better KL divergence with regards to the real distribution than the othertwo. Finally, we show the interest of our algorithm on two concrete cases:first on brain imaging data, then on biological nephrology data. In both casesour results are more in line with current knowledge in each field.

Gaussian Graphical Model exploration and selection in high dimension low sample size setting