10 research outputs found
Transferrin uptake may occur through detergent-resistant membrane domains at the cytopharynx of Trypanosoma cruzi epimastigote forms
Drawbacks and benefits associated with inter-organizational collaboration along the discovery-development-delivery continuum: a cancer research network case study
Tree diameter structural diversity in Central European forests with Abies alba and Fagus sylvatica: managed versus unmanaged forest stands
Missing Data Augmentation for Bayesian Exponential Random Multi-Graph Models
In this paper we present an estimation algorithm for Bayesian multiplex exponential random graphs (BmERGMs) under missing network data. Social actors are often connected with more than one type of relation, thus forming a multiplex network. It is important to consider these multiplex structures simultaneously when analyzing a multiplex network. The importance of proper models of multiplex network structures is even more pronounced under the issue of missing network data. The proposed algorithm is able to estimate BmERGMs under missing data and can be used to obtain proper multiple imputations for multiplex network structures. It is an extension of Bayesian exponential random graphs (BERGMs) as implemented in the Bergm package in R. We demonstrate the algorithm on a well known example, with and without artificially simulated missing data