Keywords: Bayesian analysis, Exponential random graph models, Monte Carlo methods
Webpages:
https://CRAN.R-project.org/package=Bergm Exponential random graph models (ERGMs) are a very important family of statistical models for analyzing network data. From a computational point of view, ERGMs are extremely difficult to handle since their normalising constant, which depends on model parameters, is intractable. In this talk, we show how parameter inference can be carried out in a Bayesian framework using MCMC strategies which circumvents the need to calculate the normalising constants.
The new version of the
Bergm package for
R (Caimo and Friel
2014) provides a comprehensive framework for Bayesian analysis for ERGMs useing the approximate exchange algorithm (Caimo and Friel
2011) and calibration of the pseudo-posterior distribution (Bouranis, Friel, and Maire
2015) to sample from the ERGM parameter posterior distribution. The package can also supply graphical Bayesian goodness-of-fit procedures that address the issue of model adequacy.
This talk will have a strong focus on the main practical implementation features of the software that will be described by the analysis of real network data (with various applications in Neuroscience and Organisation Science).
References Bouranis, L., N. Friel, and F. Maire. 2015. “Bayesian Inference for Misspecified Exponential Random Graph Models.”
arXiv Preprint arXiv:1510.00934.
Caimo, A., and N. Friel. 2011. “Bayesian Inference for Exponential Random Graph Models.”
Social Networks 33 (1): 41–55.
———. 2014. “Bergm: Bayesian Exponential Random Graphs in R.”
Journal of Statistical Software 61 (2): 1–25.
