2017/03/15 by Alberto Caimo, Nial Friel, Caimo, Alberto +1
Computer Science · Mathematics · Physics and Astronomy · #Bayesian Modeling and Causal Inference #Complex Network Analysis Techniques #Computation (stat.CO) #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods
paper · pdf · doi:10.48550/arxiv.1703.05144
openalex publication_date 2017/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Bergm package provides a comprehensive framework for Bayesian inference using Markov chain Monte Carlo (MCMC) algorithms. It can also supply graphical Bayesian goodness-of-fit procedures that address the issue of model adequacy. The package is simple to use and represents an attractive way of analysing network data as it offers the advantage of a complete probabilistic treatment of uncertainty. Bergm is based on the ergm package and therefore it makes use of the same model set-up and network simulation algorithms. The Bergm package has been continually improved in terms of speed performance over the last years and now offers the end-user a feasible option for carrying out Bayesian inference for networks with several thousands of nodes.