Poster Open Access

Inferring network statistics from high-dimensional undersampled time-course data

Linzner, Dominik; Koepply, Heinz

Reconstructing networks from current high-dimensional datasets is a notoriously ill-posed problem. For this reason we want to focus on more general statistical properties, as finding the degree distribution or sparsity. We consider sub-systems of continuously valued random variables embedded in a larger hidden system. In this scenario, also referred to as undersampling, the dynamics of the sub-systems are influenced by the hidden system. With increasing number of connections between sub-system and hidden system these effects can be treated statistically. Analysing these effects then allows for inference of statistical properties of the hidden system.

Files (868.0 kB)
Name Size
868.0 kB Download


Cite as