Approximate Bayesian Computation for Exponential Random Graph Models for Large Social Networks

作者：Wang Jing; Atchade Yves F^*

来源：Communications in Statistics - Simulation and Computation, 2014, 43(2): 359-377.

DOI：10.1080/03610918.2012.703359

• Bayesian inference
• Exponential random graph model
• Intractable normalizing constants
• Markov chain Monte Carlo

We consider the issue of sampling from the posterior distribution of exponential random graph (ERG) models and other statistical models with intractable normalizing constants. Existing methods based on exact sampling are either infeasible or require very long computing time. We study a class of approximate Markov chain Monte Carlo (MCMC) sampling schemes that deal with this issue. We also develop a new Metropolis-Hastings kernel to sample sparse large networks from ERG models. We illustrate the proposed methods on several examples.

• 出版日期2014-1-1