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.

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