Fitting Exponential Random Graph Models via Maximum Likelihood

Wednesday, November 19, 2003 - 4:00pm - 5:00pm
Lind 409
David Hunter (The Pennsylvania State University)
There is increasing interest in modeling network data using exponential random graph models (ERGMs). Fitting these models using traditional methods such as maximum likelihood is difficult if not impossible due to the fact that evaluation of the likelihood function involves a summation with a very large number of terms. This talk discusses a method that uses stochastic approximation of the likelihood function based on a Markov chain Monte Carlo (MCMC) approach. An alternative approach known as maximum pseudolikelihood is also discussed.