  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).
