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

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Title: Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution
Authors: Bouranis, Lampros
Friel, Nial
Maire, Florian
Permanent link: http://hdl.handle.net/10197/8564
Date: Jul-2017
Abstract: Exponential random graph models are an important tool in the statistical analysis of data. However, Bayesian parameter estimation for these models is extremely challenging, since evaluation of the posterior distribution typically involves 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Elsevier
Journal: Social Networks
Volume: 50
Start page: 98
End page: 108
Copyright (published version): 2017 Elsevier
Keywords: Machine learningStatisticsExponential random graph modelsIntractable normalizing constantsLarge networksLogistic regressionPseudolikelihoodTractable approximation
DOI: 10.1016/j.socnet.2017.03.013
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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