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Bayesian ERGMs -- computational and modelling challenges

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SNA - Theoretical foundations for statistical network analysis

Recent research in statistical social network analysis has demonstrated the advantages and effectiveness of Bayesian approaches to network data. In fact, Bayesian exponential random graph models (BERGMs) are becoming increasingly popular as techniques for modelling relational data in wide range of research areas. However, the applicability of these models in real-world settings is limited by computational complexity. In this seminar we review some of the most recent computational methods for estimating BERG Ms as well as extended ERGM -based modelling frameworks for dynamic and heterogenous social networks.

This talk is part of the Isaac Newton Institute Seminar Series series.

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