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Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution

Authors: 

Lampros Bouranis, Nial Friel, Florian Maire

Publication Type: 
Refereed Original Article
Abstract: 
Exponential random graph models are an important tool in the statistical analysis of data. However,Bayesianparameterestimationforthesemodelsisextremelychallenging,sinceevaluationof theposterior distributiontypicallyinvolves the calculation of an intractable normalizing constant. This barrier motivates the consideration of tractable approximations to the likelihood function, such as the pseudolikelihood function, which offers an approach to constructing such an approximation. Naive implementation of what we term a pseudo-posterior resulting from replacing the likelihood function in the posterior distribution by the pseudolikelihood is likely to give misleading inferences. We provide practical guidelines to correct a sample from such a pseudo-posterior distribution so that it is approximately distributed from the target posterior distribution and discuss the computational and statistical efficiency that result from this approach. We illustrate our methodology through the analysis of real-world graphs. Comparisons against the approximate exchange algorithm of Caimo and Friel (2011) are provided, followed by concluding remarks.
Digital Object Identifer (DOI): 
10.1016/j.socnet
Publication Status: 
Published
Publication Date: 
18/07/2017
Journal: 
Social Networks
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No