Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges
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Exponential random graph models (ERGMs) are flexible probabilistic frameworks to model statistical networks through a variety of network summary statistics. Conventional Bayesian estimation for ERGMs involves iteratively exchanging with an auxiliary variable d... ...
