Improving network inference by overcoming statistical limitations A reliable inference of networks from data is of key interest in many scientific fields. Several methods have been suggested in the literature to reliably determine links in a network. These techniques rely on statistical methods typically controlling the number of false positive links but not considering false negative links. In this thesis new methodologies to improve network inference are suggested. Initial analyses demonstrate the impact of falsepositive and false negative conclusions about the presence or absence of links on the resulting inferred network. Consequently revealing the importance of making well considered choices leads to suggest new approaches to enhance existing network reconstruction methods. A simulation study presented in Chapter 3 shows that different values to balance false positive and false negative conclusions about links should be used in order to reliably estimate network characteristics. The existence of type I and type II errors in the reconstructed network also called biased network is…