University of Washington - Department of Statistics
Advisor: Mark Handcock
Exponential-family random graph model (ERGM) has been widely applied in the fields of social network analysis, genetics (like protein interaction networks), information theory and more broadly. Because of the intractability of the likelihood function, Markov Chain Monte-Carlo (MCMC) algorithms are typically applied to approximate the likelihood (Geyer and Thompson 1992). However, ERGMs still suffer from inferential degeneracy and computational deficiency. In this study, we apply Bayesian inference to ERGM to resolve model degeneracy and bias-reduction problems. We implement efficient MCMC algorithms for parameter estimation. We particularly are interested in conjugate priors of exponential families and the conjugacy properties of ERGM. We carry out simulation studies to show the superiority of the estimators under Bayesian framework over those based on Monte-Carlo likelihood approximation and pseudo-likelihood.