Bayesian model selection for exponential random graph models

Details Bayesian model selection for exponential random graph models Bayesian model selection for exponential random graph models

2012-01-11

arxiv.org/abs/1201.2337v2

Statistics

Computation

23 pages

Scientific paper

Exponential random graph models are a class of widely used exponential family models for social networks. The topological structure of an observed network is modeled by the relative prevalence of a set of local sub-graph configurations termed network statistics. One of the key tasks in the application of these models is which network statistics to include in the model. This can be thought of as statistical model selection problem. This is a very challenging problem---the posterior distribution for each model is often termed "doubly intractable" since computation of the likelihood is rarely available, but also, the evidence of the posterior is, as usual, also intractable. We present a fully Bayesian model selection method based on a Markov chain Monte Carlo algorithm of Caimo and Friel (2011) which estimates the posterior probability for each competing model as well as a possible approach for computing the model evidence.

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