Bayesian latent space models for graphs are misspecified on real data, leading to poor calibration; a new generalized posterior with adaptive regularization via prequential risk estimation improves performance and geometry choice.
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Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
Bayesian latent space models for graphs are misspecified on real data, leading to poor calibration; a new generalized posterior with adaptive regularization via prequential risk estimation improves performance and geometry choice.