Document Type
Article
Rights
This item is available under a Creative Commons License for non-commercial use only
Disciplines
Statistics
Abstract
Exponential random graph models are extremely difficult models to handle from a statistical viewpoint, since their normalising constant, which depends on model parameters, is available only in very trivial cases. We show how inference can be carried out in a Bayesian framework using a MCMC algorithm, which circumvents the need to calculate the normalising constants. We use a population MCMC approach which accelerates convergence and improves mixing of the Markov chain. This approach improves performance with respect to the Monte Carlo maximum likelihood method of Geyer and Thompson (1992).
DOI
https://doi.org/10.1016/j.socnet.2010.09.004
Recommended Citation
Caimo, A. and Friel, N. (2011), Bayesian Inference for Exponential Random Graph Models. Social Networks, 33(1), 41 – 55, 2011. doi: 10.1016/j.socnet.2010.09.004
Publication Details
Social Networks, 33(1), 41 – 55, 2011
http://www.journals.elsevier.com/social-networks/